GNN paper list survey

1. Survey

• Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018). How powerful are graph neural networks?. arXiv preprint arXiv:1810.00826. [paper] [paper_review]
• Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1), 4-24. [paper]
• Cai, H., Zheng, V. W., & Chang, K. C. C. (2018). A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE transactions on knowledge and data engineering, 30(9), 1616-1637. [paper]
• Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., ... & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI open, 1, 57-81. [paper]
• Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., ... & Li, Y. (2021). Graph neural networks for recommender systems: Challenges, methods, and directions. arXiv preprint arXiv:2109.12843, 1, 46-58. [paper]
• Rampášek, L., Galkin, M., Dwivedi, V. P., Luu, A. T., Wolf, G., & Beaini, D. (2022). Recipe for a general, powerful, scalable graph transformer. Advances in Neural Information Processing Systems, 35, 14501-14515. [paper]
• Veličković, P. (2023). Everything is connected: Graph neural networks. Current Opinion in Structural Biology, 79, 102538. [paper]
• Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., & Philip, S. Y. (2022). A survey on heterogeneous graph embedding: methods, techniques, applications and sources. IEEE Transactions on Big Data, 9(2), 415-436. [paper]
• Fortunato, S. (2010). Community detection in graphs. Physics reports, 486(3-5), 75-174. [paper]

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2. Aggregate architecture

• DeepWalk - Online Learning of Social Representations :: [ paper_review ]

• node2vec: Scalable Feature Learning for Networks :: [ paper_review ]

• Structural Deep Network Embedding : SDNE :: [ paper review ]

• Structural Deep Clustering Network

• Link prediction for ex ante influence maximization on temporal networks

• MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

• Semi-Supervised Classification with Graph Convolutional Networks - GCN :: [ paper_review ]

• Adaptive Graph Convolutional Neural Networks - AGCN

• DeepGCNs: Can GCNs Go as Deep as CNNs? - DeepGCN

• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

• GEOM-GCN: Geometric Graph Convolutional networks

• Graph Convolutional Matrix Completion

• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

• LGCN: Large-Scale Learnable Graph Convolutional Networks

• AM-GCN: Adaptive Multi-channel Graph Convolutional Networks

• Gated Graph Sequence Neural Networks - GGNN

• Inductive Representation Learning on Large Graphs - GraphSAGE :: [ paper_review ]

• Composition-based multi-relational graph convolutional networks - CompGCN

• Modeling Relational Data with Graph Convolutional Networks - RGCN

• Graph Attention Networks - GAT :: [ paper_review ]

• Heterogeneous Graph Neural Network - HetGNN :: [ paper_review ]

• Heterogeneous graph Attention Network - HAN :: [ paper_review ]

• Hierarchical Graph Pooling with Structure Learning - HGP-SL

• Graph Transformer Networks - GTN

• Heterogeneous Graph Transformer

• Graph Inductive Biases in Transformers without Message Passing

• Do Transformer Really Perform Bad for Graph Representation - Graphormer :: [ paper_review ]

• Watch Your Step: Learning Node Embeddings via Graph Attention

• Universal Graph Transformer Self-Attention Networks

• metapath2vec: Scalable Representation Learning for Heterogeneous Networks :: [ paper_review ]

• Large Graph Property Prediction via Graph Segment Training

• TpuGraphs : A Performance Prediction Dataset on Large Tensor Computational Graphs

• Graph-bert: Only attention is needed for learning graph representations

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3. Graph Pooling

• Graph U-Nets
• Hierarchical Graph Representation Learning with Differentiable Pooling
• Graph Convolutional Networks with EigenPooling
• Self-Attention Graph Pooling
• Hierarchical Graph Capsule Network
• Understanding Attention and Generalization in Graph Neural Networks

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4. Temporal Graph Learning

• Graph Deep Learning for Time Series Forecasting :: [ Slides ]
• Using Causality-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs

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5. Community Detection

• Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks

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6.Knowledge Graph

• Translating Embeddings for Modeling Multi-relational Data
• Learning Entity and Relation Embeddings for Knowledge Graph Completion
• Temporal Dynamics-Aware Adversarial Attacks on Discrete-Time Dynamic Graph Models
• Unifying Large Language Models and Knowledge Graphs: A Roadmap

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7. Application

• Graph Convoultional Networks for Text Classification
• Graph-based Neural Multi-Document Summarization
• GNNExplainer : Generating Explanations for Graph Neural Networks :: [ paper_review ]
• GCN-LRP explanation: exploring latent attention of graph convolutional networks
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems - PinSAGE
• Graph Neural Networks for Social Recommendation
• Knowledge Graph Self-Supervised Rationalization for Recommendation
• Graph Convolutional Networks for Hyperspectral Image Classification - miniGCN :: [ paper_review ]
• Deep Hierarchical Graph Convolution for Election Prediction from Geospatial Census Data
• Spatial context-aware method for urban land use classification using street view images :: [ paper_review ]
• A Heterogeneous Graph-based Framework for Scalable Fraud Detection
• Heterogeneous Graph Contrastive Learning for Recommendation
• Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction
• TimeGraphs: Graph-based Temporal Reasoning
• Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection
• Graph Neural Architecture Search