From 441719abcaa6de1905ce1ecb93e02e5f0f5ed6a1 Mon Sep 17 00:00:00 2001
From: Command M <me@c0mm4nd.com>
Date: Fri, 22 Nov 2024 01:53:17 +0800
Subject: [PATCH 1/2] start using gt and embed datasets

---
 README.md                                     |  87 +--
 network_dismantling/__init__.py               |  37 +-
 network_dismantling/dismantler.py             |   6 +-
 .../edge_selectors/__init__.py                |   2 +-
 .../edge_selectors/edge_selector.py           |   5 +-
 network_dismantling/evaluators/__init__.py    |   8 +-
 .../evaluators/avg_clustering.py              |   7 +-
 .../evaluators/avg_path_length.py             |   8 +-
 network_dismantling/evaluators/evaluator.py   |  21 +-
 .../evaluators/global_effiency.py             |   9 +-
 network_dismantling/evaluators/lcc_size.py    |   8 +-
 .../evaluators/num_components.py              |  10 +-
 .../node_selectors/__init__.py                |  13 +-
 network_dismantling/node_selectors/basic.py   |  10 +-
 .../node_selectors/centrality.py              |   9 +-
 network_dismantling/node_selectors/gdm.py     |  50 +-
 network_dismantling/node_selectors/gnd.py     |  16 +-
 .../node_selectors/influence.py               |  15 +-
 .../node_selectors/node_selector.py           |   4 +-
 network_dismantling/operators/__init__.py     |   4 +-
 network_dismantling/operators/basic.py        |  15 +-
 network_dismantling/operators/operator.py     |   5 +-
 network_dismantling/selector.py               |   5 +-
 network_dismantling/utils/__init__.py         |   0
 network_dismantling/utils/data.py             | 105 ++++
 poetry.lock                                   | 505 ++++++++----------
 pyproject.toml                                |   2 -
 27 files changed, 478 insertions(+), 488 deletions(-)
 create mode 100644 network_dismantling/utils/__init__.py
 create mode 100644 network_dismantling/utils/data.py

diff --git a/README.md b/README.md
index c843156..d05b476 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # network-dismantling
 
-`network-dismantling` is a library for network dismantling, which refers to the process of strategically removing nodes from a network to disrupt its functionality. This repository provides implementations of various network dismantling algorithms, leveraging the power of PyTorch for efficient computation and flexibility.
+`network-dismantling` is a framework for network dismantling, which refers to the process of strategically removing nodes from a network to disrupt its functionality. This repository provides implementations of various network dismantling algorithms, leveraging the power of PyTorch for efficient computation and flexibility.
 
 ## Installation
 
@@ -10,14 +10,11 @@ To install the dependencies, easily install from PyPI:
 pip install network-dismantling
 ```
 
-Make sure you have PyTorch installed if you want to run a neural-network model.
+Make sure you have PyTorch and graph-tool installed if you want to run a neural-network model.
+
 You can install PyTorch from [here](https://pytorch.org/get-started/locally/).
 
-Or directly using conda:
-
-```bash
-conda install network-dismantling
-```
+You can install graph-tool from [here](https://graph-tool.skewed.de/installation.html).
 
 Alternatively, you can clone the repository and install it locally:
 
@@ -29,83 +26,7 @@ pip install -e .
 
 ## Usage
 
-```python
-# Create a test graph
-G = nx.karate_club_graph()
-
-# Initialize CoreHD strategies
-corehd_strategy = CoreHDDismantling()
-
-# Create NetworkDismantlers with different strategies
-corehd_dismantler = NetworkDismantler(corehd_strategy)
-
-# Dismantle the graph using both strategies
-num_nodes_to_remove = int(G.number_of_nodes() * 0.1)  # Remove 10% of nodes
-
-dismantled_G_corehd, removed_nodes_corehd = corehd_dismantler.dismantle(G, num_nodes_to_remove)
-
-print(f"Original graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges")
-print(f"CoreHD dismantled graph: {dismantled_G_corehd.number_of_nodes()} nodes, {dismantled_G_corehd.number_of_edges()} edges")
-print(f"CoreHD removed nodes: {removed_nodes_corehd}")
-```
-
-```python
-G = nx.karate_club_graph()
-
-# Initialize CoreGDM strategy
-coregdm_strategy = CoreGDMDismantling()
-
-# Create NetworkDismantler with CoreGDM strategy
-coregdm_dismantler = NetworkDismantler(coregdm_strategy)
-
-# Dismantle the graph using CoreGDM
-num_nodes_to_remove = int(G.number_of_nodes() * 0.1)  # Remove 10% of nodes
 
-dismantled_G_coregdm, removed_nodes_coregdm = coregdm_dismantler.dismantle(G, num_nodes_to_remove)
-
-print(f"Original graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges")
-print(f"CoreGDM dismantled graph: {dismantled_G_coregdm.number_of_nodes()} nodes, {dismantled_G_coregdm.number_of_edges()} edges")
-print(f"CoreGDM removed nodes: {removed_nodes_coregdm}")
-```
-
-```python
-    # Create a test graph
-    G = nx.karate_club_graph()
-    
-    # Initialize different dismantling strategies
-    dismantling_strategies = [
-        CollectiveInfluenceDismantling(l=2),
-        ExplosiveImmunizationDismantling(q=0.1, num_iterations=10),
-        CoreGDMDismantling(),
-        GNDDismantling(),
-        CoreHDDismantling()
-    ]
-    
-    # Initialize evaluation metrics
-    metrics = [
-        LCCSizeMetric(),
-        NumComponentsMetric(),
-        AvgPathLengthMetric(),
-        GlobalEfficiencyMetric(),
-        AvgClusteringMetric()
-    ]
-    
-    # Choose evaluation strategy
-    evaluation_strategy = RelativeChangeStrategy()
-    
-    # Create evaluator
-    evaluator = DismantlingEvaluator(metrics, evaluation_strategy)
-    
-    # Compare strategies
-    num_nodes_to_remove = int(G.number_of_nodes() * 0.1)  # Remove 10% of nodes
-    comparison_results = DismantlingEvaluator.compare_strategies(G, dismantling_strategies, num_nodes_to_remove, evaluator)
-    
-    # Print detailed results
-    for strategy_name, metrics in comparison_results.items():
-        print(f"\n{strategy_name}:")
-        for metric, value in metrics.items():
-            print(f"  {metric}: {value:.4f}")
-```
 
 ## Documentation
 
diff --git a/network_dismantling/__init__.py b/network_dismantling/__init__.py
index 8169881..35ed1ac 100644
--- a/network_dismantling/__init__.py
+++ b/network_dismantling/__init__.py
@@ -1,6 +1,37 @@
-from .dismantler import DismantlingStrategy, NetworkDismantler
-from .operators.operator import DismantlingOperator
-from .selector import ElementSelector
+def require_gt():
+    '''
+    Check graph-tool is installed
+    '''
+    try:
+        import graph_tool.all as gt
+    except ImportError:
+        raise ImportError("Please install graph-tool to use network_dismantling. You can install it as the instructions in https://graph-tool.skewed.de/installation.html")
+
+def require_pytorch():
+    '''
+    Check PyTorch is installed
+    '''
+    try:
+        import torch
+    except ImportError:
+        raise ImportError("Please install PyTorch to use network_dismantling. You can install it as the instructions in https://pytorch.org/get-started/locally/")
+
+def require_pyg():
+    '''
+    Check PyTorch Geometric is installed
+    '''
+    try:
+        import torch_geometric
+    except ImportError:
+        raise ImportError("Please install PyTorch Geometric to use network_dismantling. You can install it as the instructions in https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html")
+
+require_gt()
+require_pytorch()
+require_pyg()
+
+from network_dismantling.dismantler import DismantlingStrategy, NetworkDismantler
+from network_dismantling.operators.operator import DismantlingOperator
+from network_dismantling.selector import ElementSelector
 
 __all__ = [
     "DismantlingStrategy",
diff --git a/network_dismantling/dismantler.py b/network_dismantling/dismantler.py
index fac5676..50cdc6e 100644
--- a/network_dismantling/dismantler.py
+++ b/network_dismantling/dismantler.py
@@ -1,4 +1,4 @@
-import networkx as nx
+import graph_tool.all as gt
 from abc import abstractmethod
 from typing import Any, List, Tuple
 import torch.nn as nn
@@ -15,7 +15,7 @@ class DismantlingStrategy(nn.Module):
     """
 
     @abstractmethod
-    def dismantle(self, G: nx.Graph, num_nodes: int) -> List[int]:
+    def dismantle(self, G: gt.Graph, num_nodes: int) -> List[int]:
         """
         Dismantle the graph by removing nodes.
 
@@ -44,7 +44,7 @@ def __init__(self, selector: ElementSelector, operator: DismantlingOperator):
         self.selector = selector
         self.operator = operator
 
-    def dismantle(self, G: nx.Graph, num_elements: int) -> Tuple[nx.Graph, List[Any]]:
+    def dismantle(self, G: gt.Graph, num_elements: int) -> Tuple[gt.Graph, List[Any]]:
         """
         Dismantle the graph by removing elements.
 
diff --git a/network_dismantling/edge_selectors/__init__.py b/network_dismantling/edge_selectors/__init__.py
index 5c88c1e..6d5affd 100644
--- a/network_dismantling/edge_selectors/__init__.py
+++ b/network_dismantling/edge_selectors/__init__.py
@@ -1,3 +1,3 @@
-from .edge_selector import EdgeSelector
+from network_dismantling.edge_selectors.edge_selector import EdgeSelector
 
 __all__ = ["EdgeSelector"]
diff --git a/network_dismantling/edge_selectors/edge_selector.py b/network_dismantling/edge_selectors/edge_selector.py
index 5cb9362..f24466e 100644
--- a/network_dismantling/edge_selectors/edge_selector.py
+++ b/network_dismantling/edge_selectors/edge_selector.py
@@ -1,5 +1,4 @@
-import networkx as nx
-
+import graph_tool.all as gt
 from abc import abstractmethod
 from typing import Any, List, Tuple
 from network_dismantling.selector import ElementSelector
@@ -13,7 +12,7 @@ class EdgeSelector(ElementSelector):
     """
 
     @abstractmethod
-    def select(self, G: nx.Graph, num_edges: int) -> List[Tuple[Any, Any]]:
+    def select(self, G: gt.Graph, num_edges: int) -> List[Tuple[Any, Any]]:
         """
         Select edges from the graph.
 
diff --git a/network_dismantling/evaluators/__init__.py b/network_dismantling/evaluators/__init__.py
index 7591322..7cfbf7a 100644
--- a/network_dismantling/evaluators/__init__.py
+++ b/network_dismantling/evaluators/__init__.py
@@ -1,7 +1,7 @@
-from .avg_clustering import AvgClusteringMetric
-from .avg_path_length import AvgPathLengthMetric
-from .global_effiency import GlobalEfficiencyMetric
-from .evaluator import (
+from network_dismantling.evaluators.avg_clustering import AvgClusteringMetric
+from network_dismantling.evaluators.avg_path_length import AvgPathLengthMetric
+from network_dismantling.evaluators.global_effiency import GlobalEfficiencyMetric
+from network_dismantling.evaluators.evaluator import (
     EvaluationMetric,
     EvaluationStrategy,
     RelativeChangeStrategy,
diff --git a/network_dismantling/evaluators/avg_clustering.py b/network_dismantling/evaluators/avg_clustering.py
index cf3d5e7..4adad7e 100644
--- a/network_dismantling/evaluators/avg_clustering.py
+++ b/network_dismantling/evaluators/avg_clustering.py
@@ -1,5 +1,4 @@
-import networkx as nx
-
+import graph_tool.all as gt
 
 from network_dismantling.evaluators.evaluator import EvaluationMetric
 
@@ -12,14 +11,14 @@ class AvgClusteringMetric(EvaluationMetric):
     The average clustering coefficient is the average of the clustering coefficients of all nodes in the graph.
     """
 
-    def compute(self, G: nx.Graph) -> float:
+    def compute(self, G: gt.Graph) -> float:
         """
         Compute the average clustering coefficient of the graph.
 
         :param G: The graph.
         :return: The average clustering coefficient of the graph.
         """
-        return nx.average_clustering(G)
+        return gt.average_clustering(G)
 
     @property
     def name(self) -> str:
diff --git a/network_dismantling/evaluators/avg_path_length.py b/network_dismantling/evaluators/avg_path_length.py
index ce54100..b4d9c53 100644
--- a/network_dismantling/evaluators/avg_path_length.py
+++ b/network_dismantling/evaluators/avg_path_length.py
@@ -1,4 +1,4 @@
-import networkx as nx
+import graph_tool.all as gt
 
 
 from network_dismantling.evaluators.evaluator import EvaluationMetric
@@ -12,15 +12,15 @@ class AvgPathLengthMetric(EvaluationMetric):
     length is the average of the shortest path lengths between all pairs of nodes in the graph.
     """
 
-    def compute(self, G: nx.Graph) -> float:
+    def compute(self, G: gt.Graph) -> float:
         """
         Compute the average path length of the graph.
 
         :param G: The graph.
         :return: The average path length of the graph.
         """
-        lcc = max(nx.connected_components(G), key=len)
-        return nx.average_shortest_path_length(G.subgraph(lcc))
+        lcc = max(gt.connected_components(G), key=len)
+        return gt.average_shortest_path_length(G.subgraph(lcc))
 
     @property
     def name(self) -> str:
diff --git a/network_dismantling/evaluators/evaluator.py b/network_dismantling/evaluators/evaluator.py
index e8c7755..7ec3d25 100644
--- a/network_dismantling/evaluators/evaluator.py
+++ b/network_dismantling/evaluators/evaluator.py
@@ -1,4 +1,5 @@
-import networkx as nx
+import graph_tool.all as gt
+
 from typing import List, Dict
 from abc import ABC, abstractmethod
 
@@ -14,7 +15,7 @@ class EvaluationMetric(ABC):
     """
 
     @abstractmethod
-    def compute(self, G: nx.Graph) -> float:
+    def compute(self, G: gt.Graph) -> float:
         """
         Compute the evaluation metric on the graph.
 
@@ -45,8 +46,8 @@ class EvaluationStrategy(ABC):
     @abstractmethod
     def evaluate(
         self,
-        original_graph: nx.Graph,
-        dismantled_graph: nx.Graph,
+        original_graph: gt.Graph,
+        dismantled_graph: gt.Graph,
         metrics: List[EvaluationMetric],
     ) -> Dict[str, float]:
         """
@@ -67,8 +68,8 @@ class RelativeChangeStrategy(EvaluationStrategy):
 
     def evaluate(
         self,
-        original_graph: nx.Graph,
-        dismantled_graph: nx.Graph,
+        original_graph: gt.Graph,
+        dismantled_graph: gt.Graph,
         metrics: List[EvaluationMetric],
     ) -> Dict[str, float]:
         """
@@ -99,8 +100,8 @@ class AbsoluteValueStrategy(EvaluationStrategy):
 
     def evaluate(
         self,
-        original_graph: nx.Graph,
-        dismantled_graph: nx.Graph,
+        original_graph: gt.Graph,
+        dismantled_graph: gt.Graph,
         metrics: List[EvaluationMetric],
     ) -> Dict[str, float]:
         """
@@ -140,7 +141,7 @@ def __init__(self, metrics: List[EvaluationMetric], strategy: EvaluationStrategy
         self.strategy = strategy
 
     def evaluate(
-        self, original_graph: nx.Graph, dismantled_graph: nx.Graph
+        self, original_graph: gt.Graph, dismantled_graph: gt.Graph
     ) -> Dict[str, float]:
         """
         Evaluate the performance of a dismantling strategy.
@@ -153,7 +154,7 @@ def evaluate(
 
     @staticmethod
     def compare_strategies(
-        original_graph: nx.Graph,
+        original_graph: gt.Graph,
         dismantlers: List[NetworkDismantler],
         num_nodes_to_remove: int,
         evaluator: "DismantlingEvaluator",
diff --git a/network_dismantling/evaluators/global_effiency.py b/network_dismantling/evaluators/global_effiency.py
index 4da4b75..38ac10a 100644
--- a/network_dismantling/evaluators/global_effiency.py
+++ b/network_dismantling/evaluators/global_effiency.py
@@ -1,7 +1,6 @@
-import networkx as nx
+import graph_tool.all as gt
 import numpy as np
-from typing import List, Dict, Callable
-from abc import ABC, abstractmethod
+from typing import List, Dict
 
 from network_dismantling.evaluators.evaluator import EvaluationMetric
 
@@ -14,14 +13,14 @@ class GlobalEfficiencyMetric(EvaluationMetric):
     average of the inverse shortest path lengths between all pairs of nodes in the graph.
     """
 
-    def compute(self, G: nx.Graph) -> float:
+    def compute(self, G: gt.Graph) -> float:
         """
         Compute the global efficiency of the graph.
 
         :param G: The graph.
         :return: The global efficiency of the graph.
         """
-        return nx.global_efficiency(G)
+        return gt.global_efficiency(G)
 
     @property
     def name(self) -> str:
diff --git a/network_dismantling/evaluators/lcc_size.py b/network_dismantling/evaluators/lcc_size.py
index d1adcda..c107366 100644
--- a/network_dismantling/evaluators/lcc_size.py
+++ b/network_dismantling/evaluators/lcc_size.py
@@ -1,5 +1,5 @@
-import networkx as nx
-import numpy as np
+import graph_tool.all as gt
+
 from typing import List, Dict, Callable
 from abc import ABC, abstractmethod
 
@@ -13,14 +13,14 @@ class LCCSizeMetric(EvaluationMetric):
     The size of the largest connected component is a measure of the size of the largest connected subgraph of the graph.
     """
 
-    def compute(self, G: nx.Graph) -> float:
+    def compute(self, G: gt.Graph) -> float:
         """
         Compute the size of the largest connected component of the graph.
 
         :param G: The graph.
         :return: The size of the largest connected component of the graph.
         """
-        return len(max(nx.connected_components(G), key=len))
+        return len(max(gt.connected_components(G), key=len))
 
     @property
     def name(self) -> str:
diff --git a/network_dismantling/evaluators/num_components.py b/network_dismantling/evaluators/num_components.py
index 419ec19..50d153a 100644
--- a/network_dismantling/evaluators/num_components.py
+++ b/network_dismantling/evaluators/num_components.py
@@ -1,8 +1,6 @@
-import networkx as nx
-import numpy as np
-from typing import List, Dict, Callable
-from abc import ABC, abstractmethod
+import graph_tool.all as gt
 
+from typing import List, Dict
 from network_dismantling.evaluators.evaluator import EvaluationMetric
 
 
@@ -13,14 +11,14 @@ class NumComponentsMetric(EvaluationMetric):
     The number of connected components is a measure of the number of connected subgraphs in the graph.
     """
 
-    def compute(self, G: nx.Graph) -> float:
+    def compute(self, G: gt.Graph) -> float:
         """
         Compute the number of connected components of the graph.
 
         :param G: The graph.
         :return: The number of connected components of the graph.
         """
-        return nx.number_connected_components(G)
+        return gt.number_connected_components(G)
 
     @property
     def name(self) -> str:
diff --git a/network_dismantling/node_selectors/__init__.py b/network_dismantling/node_selectors/__init__.py
index 3d81310..366b7c5 100644
--- a/network_dismantling/node_selectors/__init__.py
+++ b/network_dismantling/node_selectors/__init__.py
@@ -1,8 +1,11 @@
-from .basic import BruteForce
-from .centrality import Betweenness, Degree
-from .gdm import GDM, CoreGDM
-from .gnd import GND, CoreHD
-from .influence import CollectiveInfluence, ExplosiveImmunization
+from network_dismantling.node_selectors.basic import BruteForce
+from network_dismantling.node_selectors.centrality import Betweenness, Degree
+from network_dismantling.node_selectors.gdm import GDM, CoreGDM
+from network_dismantling.node_selectors.gnd import GND, CoreHD
+from network_dismantling.node_selectors.influence import (
+    CollectiveInfluence,
+    ExplosiveImmunization,
+)
 
 __all__ = [
     "BruteForce",
diff --git a/network_dismantling/node_selectors/basic.py b/network_dismantling/node_selectors/basic.py
index 03dc269..387f008 100644
--- a/network_dismantling/node_selectors/basic.py
+++ b/network_dismantling/node_selectors/basic.py
@@ -1,5 +1,5 @@
-import networkx as nx
 import itertools
+import graph_tool.all as gt
 from typing import List
 
 from .node_selector import NodeSelector
@@ -24,7 +24,7 @@ def __init__(self, target_size_ratio=0.1, max_depth=None):
         self.target_size_ratio = target_size_ratio
         self.max_depth = max_depth
 
-    def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
+    def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
         """
         Select nodes to remove from the graph.
 
@@ -48,7 +48,7 @@ def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
         # If no solution found within max_depth, return the best found so far
         return self.greedy_fallback(G, num_nodes)
 
-    def is_dismantled(self, G: nx.Graph, target_size: int) -> bool:
+    def is_dismantled(self, G: gt.Graph, target_size: int) -> bool:
         """
         Check if the graph is dismantled.
 
@@ -56,10 +56,10 @@ def is_dismantled(self, G: nx.Graph, target_size: int) -> bool:
         :param target_size: The target size of the largest connected component.
         :return: True if the largest connected component is smaller than the target size, False otherwise.
         """
-        largest_cc = max(nx.connected_components(G), key=len)
+        largest_cc = max(gt.connected_components(G), key=len)
         return len(largest_cc) <= target_size
 
-    def greedy_fallback(self, G: nx.Graph, num_nodes: int) -> List[int]:
+    def greedy_fallback(self, G: gt.Graph, num_nodes: int) -> List[int]:
         """
         Greedy fallback dismantling strategy.
 
diff --git a/network_dismantling/node_selectors/centrality.py b/network_dismantling/node_selectors/centrality.py
index 1805774..3ea409a 100644
--- a/network_dismantling/node_selectors/centrality.py
+++ b/network_dismantling/node_selectors/centrality.py
@@ -1,15 +1,16 @@
+import graph_tool.all as gt
 from typing import Any, List
-import networkx as nx
+
 
 from .node_selector import NodeSelector
 
 
 class Betweenness(NodeSelector):
-    def select(self, G: nx.Graph, num_nodes: int) -> List[Any]:
-        betweenness = nx.betweenness_centrality(G)
+    def select(self, G: gt.Graph, num_nodes: int) -> List[Any]:
+        betweenness = gt.betweenness_centrality(G)
         return sorted(betweenness, key=betweenness.get, reverse=True)[:num_nodes]
 
 
 class Degree(NodeSelector):
-    def select(self, G: nx.Graph, num_nodes: int) -> List[Any]:
+    def select(self, G: gt.Graph, num_nodes: int) -> List[Any]:
         return sorted(G.degree, key=lambda x: x[1], reverse=True)[:num_nodes]
diff --git a/network_dismantling/node_selectors/gdm.py b/network_dismantling/node_selectors/gdm.py
index fc82122..cba1b25 100644
--- a/network_dismantling/node_selectors/gdm.py
+++ b/network_dismantling/node_selectors/gdm.py
@@ -1,4 +1,4 @@
-import networkx as nx
+import graph_tool.all as gt
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -119,7 +119,7 @@ def train_model(self):
             if self.early_stopper is not None and self.early_stopper.should_stop(loss):
                 break
 
-    def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
+    def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
         """
         Dismantle the graph by removing nodes based on their GDM score.
 
@@ -139,7 +139,7 @@ def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
         )[:num_nodes]
         return [node for node, _ in nodes_to_remove]
 
-    def compute_omega(self, G: nx.Graph, removed_nodes: List[int]) -> float:
+    def compute_omega(self, G: gt.Graph, removed_nodes: List[int]) -> float:
         """
         Compute the GDM dismantling efficiency of the removed nodes.
 
@@ -163,10 +163,12 @@ def extract_features(G):
         :return: A tensor of node features.
         """
         features = []
+        local_clustering = gt.local_clustering(G)
+        kcore_decomposition = gt.kcore_decomposition(G)
         for node in G.nodes():
             degree = G.degree(node)
-            clustering = nx.clustering(G, node)
-            k_core = nx.core_number(G)[node]
+            clustering = local_clustering[node]
+            k_core = kcore_decomposition[node]
             chi_square = (
                 sum((G.degree(n) - degree) ** 2 for n in G.neighbors(node)) / degree
                 if degree > 0
@@ -175,26 +177,6 @@ def extract_features(G):
             features.append([degree, clustering, k_core, chi_square])
         return torch.tensor(features, dtype=torch.float)
 
-    @staticmethod
-    def generate_synthetic_networks(num_networks, num_nodes):
-        """
-        Generate synthetic networks for training the GDM model.
-
-        :param num_networks: The number of synthetic networks to generate.
-        :param num_nodes: The number of nodes in each synthetic network.
-        :return: A list of synthetic networks.
-        """
-        networks = []
-        for _ in range(num_networks):
-            if np.random.random() < 0.33:
-                G = nx.barabasi_albert_graph(num_nodes, 3)
-            elif np.random.random() < 0.66:
-                G = nx.erdos_renyi_graph(num_nodes, 0.1)
-            else:
-                G = nx.watts_strogatz_graph(num_nodes, 4, 0.1)
-            networks.append(G)
-        return networks
-
 
 class GCNScoreModel(nn.Module):
     """
@@ -250,13 +232,13 @@ def __init__(
         self.early_stopper = early_stopper
         self.train_model()
 
-    def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
+    def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
         nodes_to_remove = []
         G_copy = G.copy()
 
         while len(nodes_to_remove) < num_nodes:
             # Get 2-core of the network
-            two_core = nx.k_core(G_copy, k=2)
+            two_core = gt.k_core(G_copy, k=2)
             if len(two_core) == 0:
                 # If 2-core is empty, remove nodes from the remaining graph
                 remaining_nodes = list(G_copy.nodes())
@@ -289,7 +271,7 @@ def train_model(self):
         for epoch in tqdm(range(100), desc="Training CoreGDM model"):
             total_loss = 0
             for G in train_networks:
-                two_core = nx.k_core(G, k=2)
+                two_core = gt.k_core(G, k=2)
                 if len(two_core) == 0:
                     continue
 
@@ -318,9 +300,9 @@ def train_model(self):
 
     def prepare_data(self, G):
         features = []
-        clustering_mapping = nx.clustering(G)
-        eigenvector_centrality_mapping = nx.eigenvector_centrality_numpy(G)
-        core_number_mapping = nx.core_number(G)
+        clustering_mapping = gt.clustering(G)
+        eigenvector_centrality_mapping = gt.eigenvector_centrality_numpy(G)
+        core_number_mapping = gt.core_number(G)
         for node in G.nodes():
             degree = G.degree(node)
             clustering = clustering_mapping[node]
@@ -338,10 +320,10 @@ def generate_synthetic_networks(num_networks, num_nodes):
         networks = []
         for _ in range(num_networks):
             if np.random.random() < 0.33:
-                G = nx.barabasi_albert_graph(num_nodes, 3)
+                G = gt.barabasi_albert_graph(num_nodes, 3)
             elif np.random.random() < 0.66:
-                G = nx.erdos_renyi_graph(num_nodes, 0.1)
+                G = gt.erdos_renyi_graph(num_nodes, 0.1)
             else:
-                G = nx.watts_strogatz_graph(num_nodes, 4, 0.1)
+                G = gt.watts_strogatz_graph(num_nodes, 4, 0.1)
             networks.append(G)
         return networks
diff --git a/network_dismantling/node_selectors/gnd.py b/network_dismantling/node_selectors/gnd.py
index d14db36..0e88672 100644
--- a/network_dismantling/node_selectors/gnd.py
+++ b/network_dismantling/node_selectors/gnd.py
@@ -1,8 +1,8 @@
-import networkx as nx
+import graph_tool.all as gt
 import numpy as np
 from typing import List
-from scipy.sparse import csr_matrix  # type: ignore
-from tqdm import tqdm  # type: ignore
+from scipy.sparse import csr_matrix
+from tqdm import tqdm
 
 from .node_selector import NodeSelector
 
@@ -27,7 +27,7 @@ def __init__(self, c=1, epsilon=0.01, max_iterations=100):
         self.epsilon = epsilon
         self.max_iterations = max_iterations
 
-    def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
+    def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
         """
         Dismantle the graph by removing nodes based on their GND score.
 
@@ -35,7 +35,7 @@ def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
         :param num_nodes: The number of nodes to remove.
         :return: A list of node indices to remove.
         """
-        A = nx.adjacency_matrix(G)
+        A = gt.adjacency_matrix(G)
         N = A.shape[0]
         D = np.array(A.sum(axis=1)).flatten()
         L = csr_matrix(np.diag(D) - A)
@@ -63,7 +63,7 @@ class CoreHD(NodeSelector):
     to. The nodes with the highest core number are removed first.
     """
 
-    def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
+    def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
         """
         Dismantle the graph by removing nodes based on their core number.
 
@@ -71,7 +71,7 @@ def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
         :param num_nodes: The number of nodes to remove.
         :return: A list of node indices to remove.
         """
-        core_numbers = nx.core_number(G)
+        core_numbers = gt.core_number(G)
         degrees = dict(G.degree())
 
         nodes_to_remove = []
@@ -86,7 +86,7 @@ def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
             G.remove_node(node_to_remove)
 
             # Update core numbers and degrees
-            core_numbers = nx.core_number(G)
+            core_numbers = gt.core_number(G)
             degrees = dict(G.degree())
 
         return nodes_to_remove
diff --git a/network_dismantling/node_selectors/influence.py b/network_dismantling/node_selectors/influence.py
index 37ef0e9..1dabcef 100644
--- a/network_dismantling/node_selectors/influence.py
+++ b/network_dismantling/node_selectors/influence.py
@@ -1,8 +1,9 @@
+import graph_tool.all as gt
+
 from collections import defaultdict
 from typing import List
-import networkx as nx
 
-from .node_selector import NodeSelector
+from network_dismantling.node_selectors.node_selector import NodeSelector
 
 
 class CollectiveInfluence(NodeSelector):
@@ -21,7 +22,7 @@ def __init__(self, l=2):  # noqa: E741
         """
         self.l = l  # Ball radius
 
-    def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
+    def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
         """
         Dismantle the graph by removing nodes based on their collective influence.
 
@@ -52,7 +53,7 @@ def compute_collective_influence(self, G):
         ci_scores = {}
         for node in G.nodes():
             ki = G.degree(node)
-            ball = nx.single_source_shortest_path_length(G, node, cutoff=self.l)
+            ball = gt.single_source_shortest_path_length(G, node, cutoff=self.l)
             ball_boundary = [n for n in ball if ball[n] == self.l]
             ci = (ki - 1) * sum(G.degree(v) - 1 for v in ball_boundary)
             ci_scores[node] = ci
@@ -78,7 +79,7 @@ def __init__(self, q=0.1, num_iterations=10):
         self.q = q  # Fraction of nodes to remove in each iteration
         self.num_iterations = num_iterations
 
-    def select(self, G: nx.Graph, num_nodes: int) -> List[int]:
+    def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
         """
         Dismantle the graph by removing nodes based on their explosive immunization score.
 
@@ -112,7 +113,7 @@ def compute_ei_scores(self, G):
         """
         scores = defaultdict(float)
         for _ in range(self.num_iterations):
-            components = list(nx.connected_components(G))
+            components = list(gt.connected_components(G))
             for component in components:
                 if len(component) > 1:
                     subgraph = G.subgraph(component)
@@ -131,5 +132,5 @@ def compute_s(self, G, node):
         """
         G_copy = G.copy()
         G_copy.remove_node(node)
-        new_components = list(nx.connected_components(G_copy))
+        new_components = list(gt.connected_components(G_copy))
         return sum(len(c) * (len(c) - 1) for c in new_components)
diff --git a/network_dismantling/node_selectors/node_selector.py b/network_dismantling/node_selectors/node_selector.py
index ed97388..b922106 100644
--- a/network_dismantling/node_selectors/node_selector.py
+++ b/network_dismantling/node_selectors/node_selector.py
@@ -1,4 +1,4 @@
-import networkx as nx
+import graph_tool.all as gt
 
 from abc import abstractmethod
 from typing import Any, List
@@ -13,7 +13,7 @@ class NodeSelector(ElementSelector):
     """
 
     @abstractmethod
-    def select(self, G: nx.Graph, num_nodes: int) -> List[Any]:
+    def select(self, G: gt.Graph, num_nodes: int) -> List[Any]:
         """
         Select nodes from the graph.
 
diff --git a/network_dismantling/operators/__init__.py b/network_dismantling/operators/__init__.py
index 86831e9..05e2fff 100644
--- a/network_dismantling/operators/__init__.py
+++ b/network_dismantling/operators/__init__.py
@@ -1,4 +1,4 @@
-from .operator import DismantlingOperator
-from .basic import NodeRemovalOperator, EdgeRemovalOperator
+from network_dismantling.operators.operator import DismantlingOperator
+from network_dismantling.operators.basic import NodeRemovalOperator, EdgeRemovalOperator
 
 __all__ = ["DismantlingOperator", "NodeRemovalOperator", "EdgeRemovalOperator"]
diff --git a/network_dismantling/operators/basic.py b/network_dismantling/operators/basic.py
index 01a320f..eccda02 100644
--- a/network_dismantling/operators/basic.py
+++ b/network_dismantling/operators/basic.py
@@ -1,7 +1,6 @@
-from collections import deque
-import networkx as nx
-
 from typing import Any, List
+import graph_tool.all as gt
+from collections import deque
 from network_dismantling.operators.operator import DismantlingOperator
 
 
@@ -12,7 +11,7 @@ class NodeRemovalOperator(DismantlingOperator):
     This dismantling operator removes nodes from the graph.
     """
 
-    def operate(self, G: nx.Graph, elements: List[Any]) -> nx.Graph:
+    def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
         """
         Remove nodes from the graph.
 
@@ -21,11 +20,11 @@ def operate(self, G: nx.Graph, elements: List[Any]) -> nx.Graph:
         :return: The modified graph
         """
         H = G.copy()
-        H.remove_nodes_from(elements)
+        H.remove_vertex(elements)
         return H
 
 class TemporalNodeNeighborEgdesRemovalOperator(DismantlingOperator):
-    def operate(self, G: nx.Graph, elements: List[Any]) -> nx.Graph:
+    def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
         """
         Remove nodes from the graph.
 
@@ -119,7 +118,7 @@ class EdgeRemovalOperator(DismantlingOperator):
     This dismantling operator removes edges from the graph.
     """
 
-    def operate(self, G: nx.Graph, elements: List[Any]) -> nx.Graph:
+    def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
         """
         Remove edges from the graph.
 
@@ -138,7 +137,7 @@ class TemporalEdgeRemovalOperator(DismantlingOperator):
     This dismantling operator removes edges from the graph.
     """
 
-    def operate(self, G: nx.Graph, elements: List[Any]) -> nx.Graph:
+    def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
         """
         Remove edges from the graph.
 
diff --git a/network_dismantling/operators/operator.py b/network_dismantling/operators/operator.py
index cf3c237..f7f5577 100644
--- a/network_dismantling/operators/operator.py
+++ b/network_dismantling/operators/operator.py
@@ -1,11 +1,12 @@
+import graph_tool.all as gt
+
 from abc import ABC, abstractmethod
 from typing import Any, List
-import networkx as nx
 
 
 class DismantlingOperator(ABC):
     @abstractmethod
-    def operate(self, G: nx.Graph, elements: List[Any]) -> nx.Graph:
+    def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
         """
         Perform the dismantling operation on the graph.
 
diff --git a/network_dismantling/selector.py b/network_dismantling/selector.py
index fd49d05..8b65b7e 100644
--- a/network_dismantling/selector.py
+++ b/network_dismantling/selector.py
@@ -1,11 +1,12 @@
-import networkx as nx
+import graph_tool.all as gt
+
 from abc import ABC, abstractmethod
 from typing import Any, List
 
 
 class ElementSelector(ABC):
     @abstractmethod
-    def select(self, G: nx.Graph, num_elements: int) -> List[Any]:
+    def select(self, G: gt.Graph, num_elements: int) -> List[Any]:
         """
         Select elements (nodes or edges) from the graph.
 
diff --git a/network_dismantling/utils/__init__.py b/network_dismantling/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/network_dismantling/utils/data.py b/network_dismantling/utils/data.py
new file mode 100644
index 0000000..22fa316
--- /dev/null
+++ b/network_dismantling/utils/data.py
@@ -0,0 +1,105 @@
+from typing import List
+import graph_tool.all as gt
+import os
+import numpy as np
+import requests
+from tqdm import tqdm
+
+
+def _download_with_tqdm(url: str, fname: str):
+    resp = requests.get(url, stream=True)
+    total = int(resp.headers.get("content-length", 0))
+
+    with open(fname, "wb") as file, tqdm(
+        desc=fname,
+        total=total,
+        unit="iB",
+        unit_scale=True,
+        unit_divisor=1024,
+    ) as bar:
+        for data in resp.iter_content(chunk_size=1024):
+            size = file.write(data)
+            bar.update(size)
+
+
+class RealWorldDatasetFetcher:
+    def __init__(
+        self,
+        dataset_name: str,
+        dataset_path_prefix: str = "./network_dismantling_data",
+    ):
+        self.dataset_name = dataset_name + ".gt.xz"
+        self.dataset_path = os.path.abspath(
+            os.path.join(dataset_path_prefix, "realworld", dataset_name)
+        )
+
+    def fetch(self):
+        url = (
+            "https://github.com/NetworkDismantling/review/raw/refs/heads/main/dataset/test_review/"
+            + self.dataset_name
+            + ".gt.xz"
+        )
+        # download with progress bar
+        if not os.path.exists(self.dataset_path):
+            print(
+                f"Downloading {self.dataset_name} dataset and saving it to {self.dataset_path}"
+            )
+            _download_with_tqdm(url, self.dataset_path)
+            print("Download completed")
+
+    def load(self):
+        return gt.load_graph(self.dataset_path)
+
+
+class SyntheticDatasetGenerator:
+    def __init__(
+        self,
+        dataset_name: str,
+        dataset_path_prefix: str = "./network_dismantling_data",
+    ):
+        self.dataset_name = dataset_name + ".gt.xz"
+        self.dataset_path = os.path.abspath(
+            os.path.join(dataset_path_prefix, "synthetic", dataset_name)
+        )
+
+    @staticmethod
+    def generate_synthetic_networks(num_networks: int, num_nodes: int) -> List[gt.Graph]:
+        """
+        Generate synthetic networks for training the GDM model using graph-tool.
+
+        Args:
+            num_networks: The number of synthetic networks to generate
+            num_nodes: The number of nodes in each synthetic network
+        
+        Returns:
+            List of synthetic networks as graph-tool Graph objects
+        """
+        networks = []
+        for _ in range(num_networks):
+            rand = np.random.random()
+            if rand < 0.33:
+                # Barabási-Albert model using Price network
+                # m=3 new edges per vertex
+                G = gt.price_network(num_nodes, m=3, directed=False)
+                
+            elif rand < 0.66:
+                # Erdős-Rényi model 
+                # p=0.1 edge probability
+                G = gt.random_graph(num_nodes, 
+                                lambda: np.random.binomial(1, 0.1), 
+                                directed=False)
+                
+            else:
+                # Watts-Strogatz model
+                # First create regular ring lattice
+                G = gt.lattice([num_nodes], dim=1, k=4, periodic=True)
+                # Then rewire with probability 0.1
+                gt.random_rewire(G, model="erdos", 
+                            n_iter=G.num_edges()*0.1,
+                            edge_sweep=True)
+                
+            networks.append(G)
+        return networks
+
+    def load(self):
+        return gt.load_graph(self.dataset_path)
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 1147574..4fb530e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "alabaster"
@@ -13,13 +13,13 @@ files = [
 
 [[package]]
 name = "astroid"
-version = "3.3.2"
+version = "3.3.5"
 description = "An abstract syntax tree for Python with inference support."
 optional = false
 python-versions = ">=3.9.0"
 files = [
-    {file = "astroid-3.3.2-py3-none-any.whl", hash = "sha256:9f8136ce9770e0f912401b25a0f15d5c2ec20b50e99b1b413ac0778fe53ff6f1"},
-    {file = "astroid-3.3.2.tar.gz", hash = "sha256:99e9b5b602cbb005434084309213d6af32bf7a9b743c836749168b8e2b330cbd"},
+    {file = "astroid-3.3.5-py3-none-any.whl", hash = "sha256:a9d1c946ada25098d790e079ba2a1b112157278f3fb7e718ae6a9252f5835dc8"},
+    {file = "astroid-3.3.5.tar.gz", hash = "sha256:5cfc40ae9f68311075d27ef68a4841bdc5cc7f6cf86671b49f00607d30188e2d"},
 ]
 
 [package.dependencies]
@@ -41,33 +41,33 @@ dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
 [[package]]
 name = "black"
-version = "24.8.0"
+version = "24.10.0"
 description = "The uncompromising code formatter."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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-    {file = "black-24.8.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:81c6742da39f33b08e791da38410f32e27d632260e599df7245cccee2064afeb"},
-    {file = "black-24.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:707a1ca89221bc8a1a64fb5e15ef39cd755633daa672a9db7498d1c19de66a42"},
-    {file = "black-24.8.0-cp310-cp310-win_amd64.whl", hash = "sha256:d6417535d99c37cee4091a2f24eb2b6d5ec42b144d50f1f2e436d9fe1916fe1a"},
-    {file = "black-24.8.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:fb6e2c0b86bbd43dee042e48059c9ad7830abd5c94b0bc518c0eeec57c3eddc1"},
-    {file = "black-24.8.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:837fd281f1908d0076844bc2b801ad2d369c78c45cf800cad7b61686051041af"},
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 [package.dependencies]
@@ -81,118 +81,133 @@ typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
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 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "certifi"
-version = "2024.7.4"
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 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
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 [[package]]
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 [[package]]
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-version = "3.7"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.5"
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+[package.extras]
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+
 [[package]]
 name = "imagesize"
 version = "1.4.1"
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+    {file = "MarkupSafe-3.0.2-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:3169b1eefae027567d1ce6ee7cae382c57fe26e82775f460f0b2778beaad66c0"},
+    {file = "MarkupSafe-3.0.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:eb7972a85c54febfb25b5c4b4f3af4dcc731994c7da0d8a0b4a6eb0640e1d178"},
+    {file = "MarkupSafe-3.0.2-cp39-cp39-win32.whl", hash = "sha256:8c4e8c3ce11e1f92f6536ff07154f9d49677ebaaafc32db9db4620bc11ed480f"},
+    {file = "MarkupSafe-3.0.2-cp39-cp39-win_amd64.whl", hash = "sha256:6e296a513ca3d94054c2c881cc913116e90fd030ad1c656b3869762b754f5f8a"},
+    {file = "markupsafe-3.0.2.tar.gz", hash = "sha256:ee55d3edf80167e48ea11a923c7386f4669df67d7994554387f84e7d8b0a2bf0"},
 ]
 
-[package.dependencies]
-mypy-extensions = ">=1.0.0"
-tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-typing-extensions = ">=4.6.0"
-
-[package.extras]
-dmypy = ["psutil (>=4.0)"]
-install-types = ["pip"]
-mypyc = ["setuptools (>=50)"]
-reports = ["lxml"]
-
 [[package]]
 name = "mypy-extensions"
 version = "1.0.0"
@@ -397,33 +369,15 @@ files = [
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]
 
-[[package]]
-name = "networkx"
-version = "3.3"
-description = "Python package for creating and manipulating graphs and networks"
-optional = false
-python-versions = ">=3.10"
-files = [
-    {file = "networkx-3.3-py3-none-any.whl", hash = "sha256:28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2"},
-    {file = "networkx-3.3.tar.gz", hash = "sha256:0c127d8b2f4865f59ae9cb8aafcd60b5c70f3241ebd66f7defad7c4ab90126c9"},
-]
-
-[package.extras]
-default = ["matplotlib (>=3.6)", "numpy (>=1.23)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
-developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
-doc = ["myst-nb (>=1.0)", "numpydoc (>=1.7)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
-extra = ["lxml (>=4.6)", "pydot (>=2.0)", "pygraphviz (>=1.12)", "sympy (>=1.10)"]
-test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
-
 [[package]]
 name = "packaging"
-version = "24.1"
+version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "packaging-24.1-py3-none-any.whl", hash = "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124"},
-    {file = "packaging-24.1.tar.gz", hash = "sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002"},
+    {file = "packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759"},
+    {file = "packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f"},
 ]
 
 [[package]]
@@ -439,19 +393,19 @@ files = [
 
 [[package]]
 name = "platformdirs"
-version = "4.2.2"
+version = "4.3.6"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "platformdirs-4.2.2-py3-none-any.whl", hash = "sha256:2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee"},
-    {file = "platformdirs-4.2.2.tar.gz", hash = "sha256:38b7b51f512eed9e84a22788b4bce1de17c0adb134d6becb09836e37d8654cd3"},
+    {file = "platformdirs-4.3.6-py3-none-any.whl", hash = "sha256:73e575e1408ab8103900836b97580d5307456908a03e92031bab39e4554cc3fb"},
+    {file = "platformdirs-4.3.6.tar.gz", hash = "sha256:357fb2acbc885b0419afd3ce3ed34564c13c9b95c89360cd9563f73aa5e2b907"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
-type = ["mypy (>=1.8)"]
+docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
+type = ["mypy (>=1.11.2)"]
 
 [[package]]
 name = "pygments"
@@ -598,13 +552,13 @@ test = ["cython (>=3.0)", "defusedxml (>=0.7.1)", "pytest (>=8.0)", "setuptools
 
 [[package]]
 name = "sphinx-autoapi"
-version = "3.2.1"
+version = "3.3.3"
 description = "Sphinx API documentation generator"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sphinx_autoapi-3.2.1-py2.py3-none-any.whl", hash = "sha256:72fe556abc579528a46494f4fcbeaeaaf3e0b031f6514f7b496f6c36754c5430"},
-    {file = "sphinx_autoapi-3.2.1.tar.gz", hash = "sha256:1f9d56b3a98d5653d1fad5644abeed2c042cec304a126ef72c236dae4af16b90"},
+    {file = "sphinx_autoapi-3.3.3-py3-none-any.whl", hash = "sha256:5c7349b42d45a492a611cb81fb48583d5148e9eab7fc6b1f326dc9273b9191e3"},
+    {file = "sphinx_autoapi-3.3.3.tar.gz", hash = "sha256:c44fd719580e9a3684ff82019f4f7f39fc970e3030ffd325936654a6f4d31f22"},
 ]
 
 [package.dependencies]
@@ -616,9 +570,6 @@ Jinja2 = "*"
 PyYAML = "*"
 sphinx = ">=6.1.0"
 
-[package.extras]
-docs = ["furo", "sphinx", "sphinx-design"]
-
 [[package]]
 name = "sphinx-rtd-theme"
 version = "2.0.0"
@@ -748,13 +699,13 @@ test = ["pytest"]
 
 [[package]]
 name = "tomli"
-version = "2.0.1"
+version = "2.1.0"
 description = "A lil' TOML parser"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
-    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
+    {file = "tomli-2.1.0-py3-none-any.whl", hash = "sha256:a5c57c3d1c56f5ccdf89f6523458f60ef716e210fc47c4cfb188c5ba473e0391"},
+    {file = "tomli-2.1.0.tar.gz", hash = "sha256:3f646cae2aec94e17d04973e4249548320197cfabdf130015d023de4b74d8ab8"},
 ]
 
 [[package]]
@@ -770,13 +721,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.2.2"
+version = "2.2.3"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
-    {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
+    {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
+    {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
 ]
 
 [package.extras]
@@ -788,4 +739,4 @@ zstd = ["zstandard (>=0.18.0)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "981a9c4a9a879e9a99093f46c9b4cc58ca62175544981540b9b506f3290fc8e1"
+content-hash = "4212fd2bc713f8294c353dc5343e50eed2d40f32671ca93c8317070522302543"
diff --git a/pyproject.toml b/pyproject.toml
index 2befb0f..504f6a7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,11 +8,9 @@ readme = "README.md"
 
 [tool.poetry.dependencies]
 python = "^3.10"
-networkx = "^3.3"
 
 [tool.poetry.group.dev.dependencies]
 black = "*"
-mypy = "*"
 
 [tool.poetry.group.docs.dependencies]
 sphinx = "^7.3.7"

From 4f8a86186f69190de03dd0ae54ac0a74a5048cb9 Mon Sep 17 00:00:00 2001
From: Command M <me@c0mm4nd.com>
Date: Fri, 22 Nov 2024 22:09:13 +0800
Subject: [PATCH 2/2] bugfix

---
 examples/heuristic.py                         |  7 +-
 network_dismantling/__init__.py               | 50 ++++++----
 network_dismantling/dismantler.py             |  1 -
 .../edge_selectors/edge_selector.py           |  5 +-
 .../evaluators/avg_clustering.py              |  3 +-
 .../evaluators/avg_path_length.py             |  5 +-
 network_dismantling/evaluators/evaluator.py   | 12 +--
 .../evaluators/global_effiency.py             | 36 ++++++-
 network_dismantling/evaluators/lcc_size.py    |  6 +-
 .../evaluators/num_components.py              |  3 +-
 network_dismantling/node_selectors/basic.py   |  4 +-
 .../node_selectors/centrality.py              |  4 +-
 network_dismantling/node_selectors/gnd.py     | 13 +--
 .../node_selectors/influence.py               | 98 ++++++++++++-------
 .../node_selectors/node_selector.py           |  6 +-
 network_dismantling/operators/basic.py        |  6 +-
 network_dismantling/operators/operator.py     |  1 -
 network_dismantling/selector.py               |  1 -
 network_dismantling/utils/data.py             | 30 +++---
 19 files changed, 181 insertions(+), 110 deletions(-)

diff --git a/examples/heuristic.py b/examples/heuristic.py
index 601eed7..422ad9e 100644
--- a/examples/heuristic.py
+++ b/examples/heuristic.py
@@ -1,4 +1,5 @@
-import networkx as nx
+import graph_tool.all as gt
+
 from network_dismantling.node_selectors import (
     CollectiveInfluence,
     ExplosiveImmunization,
@@ -19,7 +20,7 @@
 # Example usage
 if __name__ == "__main__":
     # Create a test graph
-    G = nx.karate_club_graph()
+    G = gt.collection.data["karate"]
 
     # Initialize different dismantling strategies
     dismantlers = [
@@ -48,7 +49,7 @@
     evaluator = DismantlingEvaluator(metrics, evaluation_strategy)
 
     # Compare strategies
-    num_nodes_to_remove = int(G.number_of_nodes() * 0.1)  # Remove 10% of nodes
+    num_nodes_to_remove = int(G.num_vertices() * 0.1)  # Remove 10% of nodes
     comparison_results = DismantlingEvaluator.compare_strategies(
         G, dismantlers, num_nodes_to_remove, evaluator
     )
diff --git a/network_dismantling/__init__.py b/network_dismantling/__init__.py
index 35ed1ac..7d68daf 100644
--- a/network_dismantling/__init__.py
+++ b/network_dismantling/__init__.py
@@ -1,43 +1,57 @@
+from network_dismantling.dismantler import DismantlingStrategy, NetworkDismantler
+from network_dismantling.operators.operator import DismantlingOperator
+from network_dismantling.selector import ElementSelector
+
+
 def require_gt():
-    '''
+    """
     Check graph-tool is installed
-    '''
+    """
     try:
         import graph_tool.all as gt
-    except ImportError:
-        raise ImportError("Please install graph-tool to use network_dismantling. You can install it as the instructions in https://graph-tool.skewed.de/installation.html")
+
+        _ = gt
+    except ImportError as exc:
+        raise ImportError(
+            "Please install graph-tool to use network_dismantling. You can install it as the instructions in https://graph-tool.skewed.de/installation.html"
+        ) from exc
+
 
 def require_pytorch():
-    '''
+    """
     Check PyTorch is installed
-    '''
+    """
     try:
         import torch
-    except ImportError:
-        raise ImportError("Please install PyTorch to use network_dismantling. You can install it as the instructions in https://pytorch.org/get-started/locally/")
+
+        _ = torch
+    except ImportError as exc:
+        raise ImportError(
+            "Please install PyTorch to use network_dismantling. You can install it as the instructions in https://pytorch.org/get-started/locally/"
+        ) from exc
+
 
 def require_pyg():
-    '''
+    """
     Check PyTorch Geometric is installed
-    '''
+    """
     try:
         import torch_geometric
-    except ImportError:
-        raise ImportError("Please install PyTorch Geometric to use network_dismantling. You can install it as the instructions in https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html")
+
+        _ = torch_geometric
+    except ImportError as exc:
+        raise ImportError(
+            "Please install PyTorch Geometric to use network_dismantling. You can install it as the instructions in https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html"
+        ) from exc
+
 
 require_gt()
 require_pytorch()
 require_pyg()
 
-from network_dismantling.dismantler import DismantlingStrategy, NetworkDismantler
-from network_dismantling.operators.operator import DismantlingOperator
-from network_dismantling.selector import ElementSelector
-
 __all__ = [
     "DismantlingStrategy",
     "NetworkDismantler",
-    "EvaluationMetric",
-    "EvaluationStrategy",
     "DismantlingOperator",
     "ElementSelector",
 ]
diff --git a/network_dismantling/dismantler.py b/network_dismantling/dismantler.py
index 50cdc6e..d5c401b 100644
--- a/network_dismantling/dismantler.py
+++ b/network_dismantling/dismantler.py
@@ -23,7 +23,6 @@ def dismantle(self, G: gt.Graph, num_nodes: int) -> List[int]:
         :param num_nodes: Number of nodes to remove
         :return: List of node IDs to remove
         """
-        pass
 
 
 class NetworkDismantler:
diff --git a/network_dismantling/edge_selectors/edge_selector.py b/network_dismantling/edge_selectors/edge_selector.py
index f24466e..3c4a757 100644
--- a/network_dismantling/edge_selectors/edge_selector.py
+++ b/network_dismantling/edge_selectors/edge_selector.py
@@ -12,12 +12,11 @@ class EdgeSelector(ElementSelector):
     """
 
     @abstractmethod
-    def select(self, G: gt.Graph, num_edges: int) -> List[Tuple[Any, Any]]:
+    def select(self, G: gt.Graph, num_elements: int) -> List[Tuple[Any, Any]]:
         """
         Select edges from the graph.
 
         :param G: The input graph
-        :param num_edges: Number of edges to select
+        :param num_elements: Number of edges to select
         :return: List of selected edges
         """
-        pass
diff --git a/network_dismantling/evaluators/avg_clustering.py b/network_dismantling/evaluators/avg_clustering.py
index 4adad7e..bb7e5a8 100644
--- a/network_dismantling/evaluators/avg_clustering.py
+++ b/network_dismantling/evaluators/avg_clustering.py
@@ -18,7 +18,8 @@ def compute(self, G: gt.Graph) -> float:
         :param G: The graph.
         :return: The average clustering coefficient of the graph.
         """
-        return gt.average_clustering(G)
+        clustering = gt.local_clustering(G)
+        return clustering.a.mean()
 
     @property
     def name(self) -> str:
diff --git a/network_dismantling/evaluators/avg_path_length.py b/network_dismantling/evaluators/avg_path_length.py
index b4d9c53..f9ad705 100644
--- a/network_dismantling/evaluators/avg_path_length.py
+++ b/network_dismantling/evaluators/avg_path_length.py
@@ -19,8 +19,9 @@ def compute(self, G: gt.Graph) -> float:
         :param G: The graph.
         :return: The average path length of the graph.
         """
-        lcc = max(gt.connected_components(G), key=len)
-        return gt.average_shortest_path_length(G.subgraph(lcc))
+        lcc = gt.extract_largest_component(G)
+        distances = gt.shortest_distance(lcc)
+        return distances[distances != float("inf")].a.mean()
 
     @property
     def name(self) -> str:
diff --git a/network_dismantling/evaluators/evaluator.py b/network_dismantling/evaluators/evaluator.py
index 7ec3d25..04f9569 100644
--- a/network_dismantling/evaluators/evaluator.py
+++ b/network_dismantling/evaluators/evaluator.py
@@ -22,7 +22,6 @@ def compute(self, G: gt.Graph) -> float:
         :param G: The graph.
         :return: The value of the evaluation metric.
         """
-        pass
 
     @property
     @abstractmethod
@@ -32,7 +31,6 @@ def name(self) -> str:
 
         :return: The name of the evaluation metric.
         """
-        pass
 
 
 class EvaluationStrategy(ABC):
@@ -58,7 +56,6 @@ def evaluate(
         :param metrics: The evaluation metrics to compute.
         :return: A dictionary mapping evaluation metric names to their values.
         """
-        pass
 
 
 class RelativeChangeStrategy(EvaluationStrategy):
@@ -87,9 +84,11 @@ def evaluate(
             dismantled_value = metric.compute(dismantled_graph)
             relative_change = (dismantled_value - original_value) / original_value
             results[metric.name] = relative_change
+
         results["Removed Nodes"] = (
-            1 - dismantled_graph.number_of_nodes() / original_graph.number_of_nodes()
-        )
+            original_graph.num_vertices() - dismantled_graph.num_vertices()
+        ) / original_graph.num_vertices()
+
         return results
 
 
@@ -165,6 +164,7 @@ def compare_strategies(
                 original_graph, num_nodes_to_remove
             )
             evaluation = evaluator.evaluate(original_graph, dismantled_graph)
-            results[dismantler.__class__.__name__] = evaluation
+            strategy_name = dismantler.selector.__class__.__name__ + " with " + dismantler.operator.__class__.__name__
+            results[strategy_name] = evaluation
 
         return results
diff --git a/network_dismantling/evaluators/global_effiency.py b/network_dismantling/evaluators/global_effiency.py
index 38ac10a..12341c2 100644
--- a/network_dismantling/evaluators/global_effiency.py
+++ b/network_dismantling/evaluators/global_effiency.py
@@ -1,16 +1,15 @@
 import graph_tool.all as gt
 import numpy as np
-from typing import List, Dict
 
 from network_dismantling.evaluators.evaluator import EvaluationMetric
 
-
 class GlobalEfficiencyMetric(EvaluationMetric):
     """
     Evaluation metric that computes the global efficiency of a graph.
 
-    The global efficiency is a measure of the efficiency of information transfer in a graph. The global efficiency is the
-    average of the inverse shortest path lengths between all pairs of nodes in the graph.
+    The global efficiency is a measure of the efficiency of information transfer in a graph.
+    It is defined as the average of the inverse shortest path lengths between all pairs of nodes
+    in the graph.
     """
 
     def compute(self, G: gt.Graph) -> float:
@@ -20,7 +19,33 @@ def compute(self, G: gt.Graph) -> float:
         :param G: The graph.
         :return: The global efficiency of the graph.
         """
-        return gt.global_efficiency(G)
+        N = G.num_vertices()
+        if N <= 1:
+            return 0.0  # Efficiency is zero for graphs with one or no nodes.
+
+        # Compute the shortest distances between all pairs of nodes
+        dist_matrix = gt.shortest_distance(G, weights=None, directed=False)
+
+        # Initialize efficiency sum
+        efficiency_sum = 0.0
+
+        # Iterate over all pairs of vertices
+        for v in G.vertices():
+            # Extract distances as a NumPy array and convert to float type
+            distances = dist_matrix[v].get_array().astype(float)
+            # Exclude self-distance by setting it to infinity
+            distances[int(v)] = np.inf
+            # Compute inverse distances, handling infinities
+            with np.errstate(divide='ignore', invalid='ignore'):
+                inv_distances = 1.0 / distances
+                inv_distances[~np.isfinite(inv_distances)] = 0.0  # Set infinities and NaNs to zero
+            # Sum up the efficiencies for this vertex
+            efficiency_sum += np.sum(inv_distances)
+
+        # Calculate global efficiency
+        global_efficiency = efficiency_sum / (N * (N - 1))
+
+        return global_efficiency
 
     @property
     def name(self) -> str:
@@ -30,3 +55,4 @@ def name(self) -> str:
         :return: The name of the evaluation metric.
         """
         return "Global Efficiency"
+    
\ No newline at end of file
diff --git a/network_dismantling/evaluators/lcc_size.py b/network_dismantling/evaluators/lcc_size.py
index c107366..da24c52 100644
--- a/network_dismantling/evaluators/lcc_size.py
+++ b/network_dismantling/evaluators/lcc_size.py
@@ -1,8 +1,5 @@
 import graph_tool.all as gt
 
-from typing import List, Dict, Callable
-from abc import ABC, abstractmethod
-
 from network_dismantling.evaluators.evaluator import EvaluationMetric
 
 
@@ -20,7 +17,8 @@ def compute(self, G: gt.Graph) -> float:
         :param G: The graph.
         :return: The size of the largest connected component of the graph.
         """
-        return len(max(gt.connected_components(G), key=len))
+        lcc = gt.extract_largest_component(G)
+        return lcc.num_vertices()
 
     @property
     def name(self) -> str:
diff --git a/network_dismantling/evaluators/num_components.py b/network_dismantling/evaluators/num_components.py
index 50d153a..d799d9c 100644
--- a/network_dismantling/evaluators/num_components.py
+++ b/network_dismantling/evaluators/num_components.py
@@ -18,7 +18,8 @@ def compute(self, G: gt.Graph) -> float:
         :param G: The graph.
         :return: The number of connected components of the graph.
         """
-        return gt.number_connected_components(G)
+        _, hist = gt.label_components(G)
+        return len(hist)
 
     @property
     def name(self) -> str:
diff --git a/network_dismantling/node_selectors/basic.py b/network_dismantling/node_selectors/basic.py
index 387f008..b75d1a1 100644
--- a/network_dismantling/node_selectors/basic.py
+++ b/network_dismantling/node_selectors/basic.py
@@ -56,8 +56,8 @@ def is_dismantled(self, G: gt.Graph, target_size: int) -> bool:
         :param target_size: The target size of the largest connected component.
         :return: True if the largest connected component is smaller than the target size, False otherwise.
         """
-        largest_cc = max(gt.connected_components(G), key=len)
-        return len(largest_cc) <= target_size
+        lcc = gt.extract_largest_component(G)
+        return lcc.num_vertices() <= target_size
 
     def greedy_fallback(self, G: gt.Graph, num_nodes: int) -> List[int]:
         """
diff --git a/network_dismantling/node_selectors/centrality.py b/network_dismantling/node_selectors/centrality.py
index 3ea409a..c4d4a06 100644
--- a/network_dismantling/node_selectors/centrality.py
+++ b/network_dismantling/node_selectors/centrality.py
@@ -7,8 +7,8 @@
 
 class Betweenness(NodeSelector):
     def select(self, G: gt.Graph, num_nodes: int) -> List[Any]:
-        betweenness = gt.betweenness_centrality(G)
-        return sorted(betweenness, key=betweenness.get, reverse=True)[:num_nodes]
+        betweenness = gt.betweenness(G)[0]
+        return sorted(enumerate(betweenness), key=lambda x: x[1], reverse=True)[:num_nodes]
 
 
 class Degree(NodeSelector):
diff --git a/network_dismantling/node_selectors/gnd.py b/network_dismantling/node_selectors/gnd.py
index 0e88672..846f2ae 100644
--- a/network_dismantling/node_selectors/gnd.py
+++ b/network_dismantling/node_selectors/gnd.py
@@ -35,7 +35,7 @@ def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
         :param num_nodes: The number of nodes to remove.
         :return: A list of node indices to remove.
         """
-        A = gt.adjacency_matrix(G)
+        A = gt.adjacency(G)
         N = A.shape[0]
         D = np.array(A.sum(axis=1)).flatten()
         L = csr_matrix(np.diag(D) - A)
@@ -71,11 +71,12 @@ def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
         :param num_nodes: The number of nodes to remove.
         :return: A list of node indices to remove.
         """
-        core_numbers = gt.core_number(G)
-        degrees = dict(G.degree())
-
         nodes_to_remove = []
+
         for _ in tqdm(range(num_nodes), desc="CoreHD Dismantling"):
+            core_numbers = dict(enumerate(gt.kcore_decomposition(G).get_array()))
+            degrees = G.degree_property_map()
+
             max_core = max(core_numbers.values())
             candidates = [
                 node for node, core in core_numbers.items() if core == max_core
@@ -85,8 +86,4 @@ def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
             nodes_to_remove.append(node_to_remove)
             G.remove_node(node_to_remove)
 
-            # Update core numbers and degrees
-            core_numbers = gt.core_number(G)
-            degrees = dict(G.degree())
-
         return nodes_to_remove
diff --git a/network_dismantling/node_selectors/influence.py b/network_dismantling/node_selectors/influence.py
index 1dabcef..1486544 100644
--- a/network_dismantling/node_selectors/influence.py
+++ b/network_dismantling/node_selectors/influence.py
@@ -5,8 +5,7 @@
 
 from network_dismantling.node_selectors.node_selector import NodeSelector
 
-
-class CollectiveInfluence(NodeSelector):
+class CollectiveInfluence:
     """
     Dismantling strategy that removes nodes based on their collective influence.
 
@@ -22,14 +21,15 @@ def __init__(self, l=2):  # noqa: E741
         """
         self.l = l  # Ball radius
 
-    def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
+    def select(self, G: gt.Graph, num_elements: int) -> List[int]:
         """
         Dismantle the graph by removing nodes based on their collective influence.
 
         :param G: The graph to dismantle.
-        :param num_nodes: The number of nodes to remove.
+        :param num_elements: The number of nodes to remove.
         :return: A list of node indices to remove.
         """
+        num_nodes = num_elements
         nodes_to_remove: List[int] = []
         G_copy = G.copy()
 
@@ -38,12 +38,12 @@ def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
             if not ci_scores:
                 break
             node_to_remove = max(ci_scores, key=ci_scores.get)
-            nodes_to_remove.append(node_to_remove)
-            G_copy.remove_node(node_to_remove)
+            nodes_to_remove.append(int(node_to_remove))
+            G_copy.remove_vertex(node_to_remove, fast=True)
 
         return nodes_to_remove
 
-    def compute_collective_influence(self, G):
+    def compute_collective_influence(self, G: gt.Graph):
         """
         Compute the collective influence of each node in the graph.
 
@@ -51,11 +51,19 @@ def compute_collective_influence(self, G):
         :return: A dictionary with the collective influence scores of each node.
         """
         ci_scores = {}
-        for node in G.nodes():
-            ki = G.degree(node)
-            ball = gt.single_source_shortest_path_length(G, node, cutoff=self.l)
-            ball_boundary = [n for n in ball if ball[n] == self.l]
-            ci = (ki - 1) * sum(G.degree(v) - 1 for v in ball_boundary)
+        degrees = G.degree_property_map("total")
+        for node in G.vertices():
+            ki = degrees[node]
+            if ki <= 1:
+                ci_scores[node] = 0
+                continue
+            # Compute distances from node to all other nodes
+            dist_map = gt.shortest_distance(G, source=node, max_dist=self.l).a
+            # Get vertices at distance exactly l
+            ball_boundary = [v for v in G.vertices() if dist_map[int(v)] == self.l]
+            # Compute the sum over the degrees of the boundary nodes
+            sum_kj_minus1 = sum(degrees[v] - 1 for v in ball_boundary)
+            ci = (ki - 1) * sum_kj_minus1
             ci_scores[node] = ci
         return ci_scores
 
@@ -79,58 +87,80 @@ def __init__(self, q=0.1, num_iterations=10):
         self.q = q  # Fraction of nodes to remove in each iteration
         self.num_iterations = num_iterations
 
-    def select(self, G: gt.Graph, num_nodes: int) -> List[int]:
+    def select(self, G: gt.Graph, num_elements: int) -> List[int]:
         """
         Dismantle the graph by removing nodes based on their explosive immunization score.
 
         :param G: The graph to dismantle.
-        :param num_nodes: The number of nodes to remove.
+        :param num_elements: The number of nodes to remove.
         :return: A list of node indices to remove.
         """
+        num_nodes = num_elements
         nodes_to_remove: List[int] = []
         G_copy = G.copy()
+        mask = G_copy.new_vertex_property("bool", val=True)
 
         while len(nodes_to_remove) < num_nodes:
-            scores = self.compute_ei_scores(G_copy)
+            G_view = gt.GraphView(G_copy, vfilt=mask)
+            scores = self.compute_ei_scores(G_view)
             if not scores:
                 break
             candidates = sorted(scores.items(), key=lambda x: x[1], reverse=True)
             num_to_remove = min(
-                int(self.q * len(G_copy)), num_nodes - len(nodes_to_remove)
+                max(1, int(self.q * G_view.num_vertices())), num_nodes - len(nodes_to_remove)
             )
-            for node, _ in candidates[:num_to_remove]:
-                nodes_to_remove.append(node)
-                G_copy.remove_node(node)
+            for node_index, _ in candidates[:num_to_remove]:
+                nodes_to_remove.append(node_index)
+                mask[node_index] = False  # Mark node as removed
 
         return nodes_to_remove
 
-    def compute_ei_scores(self, G):
+    def compute_ei_scores(self, G: gt.GraphView):
         """
         Compute the explosive immunization score of each node in the graph.
 
-        :param G: The graph.
+        :param G: The graph view.
         :return: A dictionary with the explosive immunization scores of each node.
         """
         scores = defaultdict(float)
         for _ in range(self.num_iterations):
-            components = list(gt.connected_components(G))
-            for component in components:
+            comp, _ = gt.label_components(G)
+            # 使用 defaultdict 来存储组件
+            components = defaultdict(list)
+            for v in G.vertices():
+                comp_label = comp[v]
+                components[comp_label].append(int(v))
+            for component in components.values():
                 if len(component) > 1:
-                    subgraph = G.subgraph(component)
-                    for node in component:
-                        s = self.compute_s(subgraph, node)
-                        scores[node] += s / self.num_iterations
+                    subgraph_mask = G.new_vertex_property("bool", val=False)
+                    for idx in component:
+                        subgraph_mask[idx] = True
+                    subgraph = gt.GraphView(G, vfilt=subgraph_mask)
+                    for node_index in component:
+                        s = self.compute_s(subgraph, node_index)
+                        scores[node_index] += s / self.num_iterations
         return scores
 
-    def compute_s(self, G, node):
+    def compute_s(self, G: gt.GraphView, node_index):
         """
         Compute the explosive immunization score of a node.
 
-        :param G: The graph.
-        :param node: The node.
+        :param G: The graph view.
+        :param node_index: The index of the node.
         :return: The explosive immunization score of the node.
         """
-        G_copy = G.copy()
-        G_copy.remove_node(node)
-        new_components = list(gt.connected_components(G_copy))
-        return sum(len(c) * (len(c) - 1) for c in new_components)
+        mask = G.new_vertex_property("bool")
+        mask.a = G.get_vertex_filter()[0].a.copy()
+        mask[node_index] = False  # Simulate removal of the node
+        G_copy = gt.GraphView(G, vfilt=mask)
+        
+        if G_copy.num_vertices() == 0:
+            return 0  # return 0 when graph is empty
+
+        comp, _ = gt.label_components(G_copy)
+        component_sizes = defaultdict(int)
+        for v in G_copy.vertices():
+            comp_label = comp[v]
+            component_sizes[comp_label] += 1
+        return sum(size * (size - 1) for size in component_sizes.values())
+    
\ No newline at end of file
diff --git a/network_dismantling/node_selectors/node_selector.py b/network_dismantling/node_selectors/node_selector.py
index b922106..46e736b 100644
--- a/network_dismantling/node_selectors/node_selector.py
+++ b/network_dismantling/node_selectors/node_selector.py
@@ -13,12 +13,12 @@ class NodeSelector(ElementSelector):
     """
 
     @abstractmethod
-    def select(self, G: gt.Graph, num_nodes: int) -> List[Any]:
+    def select(self, G: gt.Graph, num_elements: int) -> List[Any]:
         """
         Select nodes from the graph.
 
         :param G: The input graph
-        :param num_nodes: Number of nodes to select
+        :param num_elements: Number of nodes to select
         :return: List of selected nodes
         """
-        pass
+
diff --git a/network_dismantling/operators/basic.py b/network_dismantling/operators/basic.py
index eccda02..22ecbdb 100644
--- a/network_dismantling/operators/basic.py
+++ b/network_dismantling/operators/basic.py
@@ -23,6 +23,7 @@ def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
         H.remove_vertex(elements)
         return H
 
+
 class TemporalNodeNeighborEgdesRemovalOperator(DismantlingOperator):
     def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
         """
@@ -38,6 +39,7 @@ def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
 
     # cannot use the original data, because the original data does not have the message, time, and message_type attributes
     def dismantling_from_nodes(
+        self,
         G,
         nodes,
         message_type_label="message_type",
@@ -130,6 +132,7 @@ def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
         H.remove_edges_from(elements)
         return H
 
+
 class TemporalEdgeRemovalOperator(DismantlingOperator):
     """
     Dismantling operator that removes edges from the graph.
@@ -150,6 +153,7 @@ def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
         return H
 
     def dismantling_from_edges(
+        self,
         G,
         edges,
         message_type_label="message_type",
@@ -215,4 +219,4 @@ def dismantling_from_edges(
                         queue.append(v)
                         processed_edges.add(v)
 
-        return G_dismantled
\ No newline at end of file
+        return G_dismantled
diff --git a/network_dismantling/operators/operator.py b/network_dismantling/operators/operator.py
index f7f5577..d61df8c 100644
--- a/network_dismantling/operators/operator.py
+++ b/network_dismantling/operators/operator.py
@@ -14,4 +14,3 @@ def operate(self, G: gt.Graph, elements: List[Any]) -> gt.Graph:
         :param elements: Elements to operate on
         :return: The modified graph
         """
-        pass
diff --git a/network_dismantling/selector.py b/network_dismantling/selector.py
index 8b65b7e..af00993 100644
--- a/network_dismantling/selector.py
+++ b/network_dismantling/selector.py
@@ -14,4 +14,3 @@ def select(self, G: gt.Graph, num_elements: int) -> List[Any]:
         :param num_elements: Number of elements to select
         :return: List of selected elements
         """
-        pass
diff --git a/network_dismantling/utils/data.py b/network_dismantling/utils/data.py
index 22fa316..e526737 100644
--- a/network_dismantling/utils/data.py
+++ b/network_dismantling/utils/data.py
@@ -63,14 +63,16 @@ def __init__(
         )
 
     @staticmethod
-    def generate_synthetic_networks(num_networks: int, num_nodes: int) -> List[gt.Graph]:
+    def generate_synthetic_networks(
+        num_networks: int, num_nodes: int
+    ) -> List[gt.Graph]:
         """
         Generate synthetic networks for training the GDM model using graph-tool.
 
         Args:
             num_networks: The number of synthetic networks to generate
             num_nodes: The number of nodes in each synthetic network
-        
+
         Returns:
             List of synthetic networks as graph-tool Graph objects
         """
@@ -81,25 +83,25 @@ def generate_synthetic_networks(num_networks: int, num_nodes: int) -> List[gt.Gr
                 # Barabási-Albert model using Price network
                 # m=3 new edges per vertex
                 G = gt.price_network(num_nodes, m=3, directed=False)
-                
+
             elif rand < 0.66:
-                # Erdős-Rényi model 
+                # Erdős-Rényi model
                 # p=0.1 edge probability
-                G = gt.random_graph(num_nodes, 
-                                lambda: np.random.binomial(1, 0.1), 
-                                directed=False)
-                
+                G = gt.random_graph(
+                    num_nodes, lambda: np.random.binomial(1, 0.1), directed=False
+                )
+
             else:
                 # Watts-Strogatz model
                 # First create regular ring lattice
-                G = gt.lattice([num_nodes], dim=1, k=4, periodic=True)
+                G = gt.lattice([num_nodes], periodic=True)
                 # Then rewire with probability 0.1
-                gt.random_rewire(G, model="erdos", 
-                            n_iter=G.num_edges()*0.1,
-                            edge_sweep=True)
-                
+                gt.random_rewire(
+                    G, model="erdos", n_iter=G.num_edges() * 0.1, edge_sweep=True
+                )
+
             networks.append(G)
         return networks
 
     def load(self):
-        return gt.load_graph(self.dataset_path)
\ No newline at end of file
+        return gt.load_graph(self.dataset_path)