CogDL是由清华大学计算机系知识工程实验室（KEG）开发的基于图的深度学习的研究工具，基于Python语言和Pytorch库。CogDL允许研究人员和开发人员可以轻松地训练和比较基线算法或自定义模型，以进行结点分类，链接预测，图分类，社区发现等基于图结构的任务。 它提供了许多流行模型的实现，包括：非图神经网络算法例如Deepwalk、LINE、Node2vec、NetMF、ProNE、methpath2vec、PTE、graph2vec、DGK等；图神经网络算法例如GCN、GAT、GraphSAGE、FastGCN、GTN、HAN、GIN、DiffPool等。它也提供了一些下游任务，包括结点分类（分为是否具有节点属性），链接预测（分为同构和异构），图分类（分有监督和⽆监督）以及为这些任务构建各种算法效果的排行榜。
CogDL的特性包括：
- 任务导向： CogDL以图上的任务为主，提供了相关的模型、数据集以及我们得到的排行榜。
- 一键运行： CogDL支持用户使用多个GPU同时运行同一个任务下多个模型在多个数据集上的多组实验。
- 多类任务： CogDL支持同构/异构网络中的节点分类和链接预测任务以及图分类任务。
- 可扩展性：用户可以基于CogDL已有的框架来实现和提交新的数据集、模型和任务。
CogDL的整体框架如上图所示，针对不同的任务，CogDL支持以下模型：
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无监督结点分类: ProNE (Zhang et al, IJCAI'19), NetMF (Qiu et al, WSDM'18), Node2vec (Grover et al, KDD'16), NetSMF (Qiu et at, WWW'19), DeepWalk (Perozzi et al, KDD'14), LINE (Tang et al, WWW'15), Hope (Ou et al, KDD'16), SDNE (Wang et al, KDD'16), GraRep (Cao et al, CIKM'15), DNGR (Cao et al, AAAI'16).
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半监督结点分类: SGC-PN (Zhao & Akoglu, 2019), Graph U-Net (Gao et al., 2019), MixHop (Abu-El-Haija et al., ICML'19), DR-GAT (Zou et al., 2019), GAT (Veličković et al., ICLR'18), DGI (Veličković et al., ICLR'19), GCN (Kipf et al., ICLR'17), GraphSAGE (Hamilton et al., NeurIPS'17), Chebyshev (Defferrard et al., NeurIPS'16).
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异构结点分类: GTN (Yun et al, NeurIPS'19), HAN (Xiao et al, WWW'19), PTE (Tang et al, KDD'15), Metapath2vec (Dong et al, KDD'17), Hin2vec (Fu et al, CIKM'17).
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链接预测: ProNE (Zhang et al, IJCAI'19), NetMF (Qiu et al, WSDM'18), Node2vec (Grover et al, KDD'16), DeepWalk (Perozzi et al, KDD'14), LINE (Tang et al, WWW'15), Hope (Ou et al, KDD'16), NetSMF (Qiu et at, WWW'19), SDNE (Wang et al, KDD'16).
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多重边链接预测: GATNE (Cen et al, KDD'19), NetMF (Qiu et al, WSDM'18), ProNE (Zhang et al, IJCAI'19), Node2vec (Grover et al, KDD'16), DeepWalk (Perozzi et al, KDD'14), LINE (Tang et al, WWW'15), Hope (Ou et al, KDD'16), GraRep (Cao et al, CIKM'15).
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无监督图分类: Infograph (Sun et al, ICLR'20), Graph2Vec (Narayanan et al, CoRR'17), DGK (Yanardag et al, KDD'15).
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有监督图分类: GIN (Xu et al, ICLR'19), DiffPool (Ying et al, NeuIPS'18), SortPool (Zhang et al, AAAI'18), PATCH_SAN (Niepert et al, ICML'16), DGCNN (Wang et al, ACM Transactions on Graphics'17).
CogDL实现了一系列不同类型的模型，下面列出了这些算法的特性。
| Algorithm | Directed | Weight | Shallow network | Matrix factorization | Sampling | Reproducibility | GPU support |
|---|---|---|---|---|---|---|---|
| DeepWalk | ✔️ | ✔️ | |||||
| LINE | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ||
| Node2vec | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ||
| SDNE | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ||
| DNGR | ✔️ | ✔️ | ✔️ | ✔️ | |||
| HOPE | ✔️ | ✔️ | ✔️ | ✔️ | |||
| GraRep | ✔️ | ✔️ | ✔️ | ||||
| NetMF | ✔️ | ✔️ | ✔️ | ✔️ | |||
| NetSMF | ✔️ | ✔️ | ✔️ | ✔️ | |||
| ProNE | ✔️ | ✔️ | ✔️ | ✔️ |
其中，在Reproducibility项为空的算法，表示setting不一致或暂时没有完全复现。
| Algorithm | Weight | Sampling | Attention | Inductive | Reproducibility | GPU support |
|---|---|---|---|---|---|---|
| Graph U-Net | ✔️ | ✔️ | ✔️ | ✔️ | ||
| MixHop | ✔️ | ✔️ | ✔️ | |||
| Dr-GAT | ✔️ | ✔️ | ✔️ | ✔️ | ||
| GAT | ✔️ | ✔️ | ✔️ | ✔️ | ||
| DGI | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | |
| GCN | ✔️ | ✔️ | ✔️ | ✔️ | ||
| GraphSAGE | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | |
| Chebyshev | ✔️ | ✔️ | ✔️ | ✔️ |
| Algorithm | Multi-Node | Multi-Edge | Attribute | Supervised | MetaPath | Reproducibility | GPU support |
|---|---|---|---|---|---|---|---|
| GATNE | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | |
| Metapath2vec | ✔️ | ✔️ | ✔️ | ||||
| PTE | ✔️ | ✔️ | |||||
| Hin2vec | ✔️ | ✔️ | ✔️ | ✔️ | |||
| GTN | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | |
| HAN | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
| Algorithm | Node feature | Unsupervised | Graph kernel | Shallow network | Reproducibility | GPU support |
|---|---|---|---|---|---|---|
| Infograph | ✔️ | ✔️ | ✔️ | ✔️ | ||
| Diffpool | ✔️ | ✔️ | ✔️ | |||
| Graph2Vec | ✔️ | ✔️ | ✔️ | ✔️ | ||
| Sortpool | ✔️ | ✔️ | ✔️ | |||
| GIN | ✔️ | ✔️ | ✔️ | |||
| PATCHY_SAN | ✔️ | ✔️ | ✔️ | ✔️ | ||
| DGCNN | ✔️ | ✔️ | ✔️ | |||
| DGK | ✔️ | ✔️ | ✔️ |
CogDL提供了一些下游任务，包括结点分类（具有或不具有结点属性），链接预测（具有或不具有属性，异构或非异构）和图分类（有监督或无监督）任务。 我们建立了几个排行榜，这些排行榜列出了各类算法在这些任务上的最新结果。
这是一个根据无监督的多标签结点分类设置而构建的排行榜，我们在几个真实的数据集上运行CogDL上的无监督表示学习算法，并将输出的表示和90％的结点标签作为经L2归一化的逻辑回归中的训练数据，使用剩余10％的标签作为测试数据，计算并按照Micro-F1的大小进行排序。
| Rank | Method | PPI | Blogcatalog | Wikipedia |
|---|---|---|---|---|
| 1 | ProNE (Zhang et al, IJCAI'19) | 26.32 | 43.63 | 57.64 |
| 2 | NetMF (Qiu et al, WSDM'18) | 24.86 | 43.49 | 58.46 |
| 3 | Node2vec (Grover et al, KDD'16) | 23.86 | 42.51 | 53.68 |
| 4 | NetSMF (Qiu et at, WWW'19) | 24.39 | 43.21 | 51.42 |
| 5 | DeepWalk (Perozzi et al, KDD'14) | 22.72 | 42.26 | 50.42 |
| 6 | LINE (Tang et al, WWW'15) | 23.15 | 39.29 | 49.83 |
| 7 | Hope (Ou et al, KDD'16) | 23.24 | 35.52 | 52.96 |
| 8 | SDNE (Wang et al, KDD'16) | 20.14 | 40.32 | 48.24 |
| 9 | GraRep (Cao et al, CIKM'15) | 20.96 | 34.35 | 51.84 |
| 10 | DNGR (Cao et al, AAAI'16) | 16.45 | 28.54 | 48.57 |
下面是几种常见的图神经网络算法在半监督结点分类任务上构建的排行榜。我们在经典的三个数据集Cora, Citeseer和Pubmed进行了实验，以Accuracy指标来评价模型的效果。
| Rank | Method | Cora | Citeseer | Pubmed |
|---|---|---|---|---|
| 1 | Graph U-Net (Gao et al., 2019) | 84.4 ± 0.6 | 73.2 ± 0.5 | 79.6 ± 0.2 |
| 2 | MixHop (Abu-El-Haija et al., ICML'19) | 81.9 ± 0.4 | 71.4 ± 0.8 | 80.8 ± 0.6 |
| 3 | DR-GAT (Zou et al., 2019) | 83.6 ± 0.5 | 72.8 ± 0.8 | 79.1 ± 0.3 |
| 4 | GAT (Veličković et al., ICLR'18) | 83.0 ± 0.7 | 72.5 ± 0.7 | 79.0 ± 0.3 |
| 5 | DGI (Veličković et al., ICLR'19) | 82.3 ± 0.6 | 71.8 ± 0.7 | 76.8 ± 0.6 |
| 6 | GCN (Kipf et al., ICLR'17) | 81.4 ± 0.5 | 70.9 ± 0.5 | 79.0 ± 0.3 |
| 7 | GraphSAGE (Hamilton et al., NeurIPS'17) | 80.1 ± 0.2 | 66.2 ± 0.4 | 76.9 ± 0.7 |
| 8 | Chebyshev (Defferrard et al., NeurIPS'16) | 79.2 ± 1.4 | 69.3 ± 1.3 | 68.5 ± 1.2 |
对于异构的结点分类任务，我们使用Macro F1来评价模型的效果。我们在GTN算法的实验设置和数据集下对所有算法进行评估。
| Rank | Method | DBLP | ACM | IMDB |
|---|---|---|---|---|
| 1 | GTN (Yun et al, NeurIPS'19) | 92.03 | 90.85 | 59.24 |
| 2 | HAN (Xiao et al, WWW'19) | 91.21 | 87.25 | 53.94 |
| 3 | GCC (Qiu et al, KDD'20) | 79.42 | 86.82 | 55.86 |
| 4 | PTE (Tang et al, KDD'15) | 78.65 | 87.44 | 48.91 |
| 5 | Metapath2vec (Dong et al, KDD'17) | 75.18 | 88.79 | 43.10 |
| 6 | Hin2vec (Fu et al, CIKM'17) | 74.31 | 84.66 | 44.04 |
对于链接预测任务，我们通过隐去数据集中10%的边，然后对隐去的边进行预测，使用ROC-AUC指标来评估模型的性能。ROC-AUC指标代表了一条随机未观察到的边对应的两个结点比一条随机不存在的边对应的两个结点更相似的概率。
| Rank | Method | PPI | Wikipedia |
|---|---|---|---|
| 1 | ProNE (Zhang et al, IJCAI'19) | 79.93 | 82.74 |
| 2 | NetMF (Qiu et al, WSDM'18) | 79.04 | 73.24 |
| 3 | Hope (Ou et al, KDD'16) | 80.21 | 68.89 |
| 4 | LINE (Tang et al, WWW'15) | 73.75 | 66.51 |
| 5 | Node2vec (Grover et al, KDD'16) | 70.19 | 66.60 |
| 6 | NetSMF (Qiu et at, WWW'19) | 68.64 | 67.52 |
| 7 | DeepWalk (Perozzi et al, KDD'14) | 69.65 | 65.93 |
| 8 | SDNE (Wang et al, KDD'16) | 54.87 | 60.72 |
对于异构链接预测任务，我们会对数据集中的某些视图下的链接进行预测，然后取Macro ROC-AUC作为评价指标。我们提出的GATNE模型是专门针对这种多视图的异构网络，而这里列举的其他方法只能处理同构网络，因此我们向这些方法分别输入不同视图下的网络，并为每种视图下的网络分别获得结点表示用于链接预测，最后同样采用Macro ROC-AUC作为评测指标。
| Rank | Method | Amazon | YouTube | |
|---|---|---|---|---|
| 1 | GATNE (Cen et al, KDD'19) | 97.44 | 84.61 | 92.30 |
| 2 | NetMF (Qiu et al, WSDM'18) | 97.72 | 82.53 | 73.75 |
| 3 | ProNE (Zhang et al, IJCAI'19) | 96.51 | 78.96 | 81.32 |
| 4 | Node2vec (Grover et al, KDD'16) | 86.86 | 74.01 | 78.30 |
| 5 | DeepWalk (Perozzi et al, KDD'14) | 92.54 | 74.31 | 60.29 |
| 6 | LINE (Tang et al, WWW'15) | 92.56 | 73.40 | 60.36 |
| 7 | Hope (Ou et al, KDD'16) | 94.39 | 74.66 | 70.61 |
| 8 | GraRep (Cao et al, CIKM'15) | 83.88 | 71.37 | 49.64 |
CogDL统一对有监督和无监督的图分类算法在相同的若干个真实的数据集上运行和评测。有监督图分类算法使用kfold对算法进行调参、训练和评测；无监督图分类算法学习到图的表示之后，将其作为输入并利用90%的图的标签作为SVM的训练数据，使用剩余10%的标签作为测试数据。两者均计算并按照Accuracy的大小进行排序。
| Rank | Method | MUTAG | IMDB-B | IMDB-M | PROTEINS | COLLAB |
|---|---|---|---|---|---|---|
| 1 | Infograph (Sun et al, ICLR'20) | 88.95 | 74.50 | 51.33 | 73.93 | 78.14 |
| 2 | GIN (Xu et al, ICLR'19) | 88.33 | 76.70 | 50.80 | 72.86 | 79.52 |
| 3 | DiffPool (Ying et al, NeuIPS'18) | 85.18 | 74.30 | 50.73 | 75.30 | 77.20 |
| 4 | SortPool (Zhang et al, AAAI'18) | 85.61 | 75.20 | 51.07 | 74.11 | 79.98 |
| 5 | Graph2Vec (Narayanan et al, CoRR'17) | 83.68 | 73.90 | 52.27 | 73.30 | 85.58 |
| 6 | PATCH_SAN (Niepert et al, ICML'16) | 85.12 | 76.00 | 46.20 | 75.50 | 75.42 |
| 7 | DGCNN (Wang et al, ACM Transactions on Graphics'17) | 83.33 | 69.50 | 46.33 | 66.67 | 77.45 |
| 8 | DGK (Yanardag et al, KDD'15) | 83.68 | 55.00 | 40.40 | 72.59 | / |
- Python 版本 >= 3.6
- PyTorch 版本 >= 1.0.0
- PyTorch Geometric (推荐)
- Deep Graph Library (可选)
CogDL安装请按照这里的说明来安装PyTorch和其他依赖项:
- https://github.com/pytorch/pytorch#installation
- https://github.com/rusty1s/pytorch_geometric/#installation
- pip install -e .
安装cogdl和其他依赖
pip install cogdl如果您想尝试尚未发布的最新CogDL功能，可以通过以下方式安装CogDL：
git clone [email protected]:THUDM/cogdl.git
cd cogdl
pip install -e .您也可以选择使用Docker。要构建Docker镜像，只需运行以下命令。
docker build --build-arg CUDA=YOUR_CUDA_VERSION --build-arg TORCH=YOUR_TORCH_VERSION --tag cogdl .请根据您的CUDA版本（或CPU）更换 YOUR_CUDA_VERSION 以及 更换 YOUR_TORCH_VERSION 为您使用的PyTorch版本。
例如，使用 CUDA 10.1 和 PyTorch 1.7.0 一起运行
docker build --build-arg CUDA=cu101 --build-arg TORCH=1.7.0 --tag cogdl .启动容器
docker run -it -v cogdl:/cogdl cogdl /bin/bash将此存储库克隆到cogdl文件夹中：
git clone https://github.com/THUDM/cogdl /cogdl注意：如果安装的Torch版本不同于1.7.0，则torchvision和torchaudio库可能存在一些问题。您可能需要手动重新安装。
您可以通过CogDL API进行各种实验，包括：build_dataset，build_model，和build_task。您也可以使用自己的数据集和模型进行实验。example/ 文件夹里提供了一些例子。
# Set hyper-parameters for experiments
args = get_default_args()
args.task = 'node_classification'
args.dataset = 'cora'
args.model = 'gcn'
# Set datasets
dataset = build_dataset(args)
args.num_features = dataset.num_features
args.num_classes = dataset.num_classes
args.num_layers = 2
# Build models
model = build_model(args)
# Train and evaluate models
task = build_task(args, dataset=dataset, model=model)
ret = task.train()基本用法可以使用 python train.py --task example_task --dataset example_dataset --model example_method 来在 example_data 上运行 example_method 并使用 example_task 来评测结果。
- --task, 运行的任务名称，像node_classification, unsupervised_node_classification, link_prediction这样来评测表示质量的下游任务。
- --dataset, 运行的数据集名称，可以是以空格分隔开的数据集名称的列表,现在支持的数据集包括 cora, citeseer, pumbed, PPI, wikipedia, blogcatalog, dblp, flickr等。
- --model, 运行的模型名称,可以是个列表，支持的模型包括 gcn, gat,deepwalk, node2vec, hope, grarep, netmf, netsmf, prone等。
如果你想在Cora数据集上运行GCN模型,并用node classification评测,可以使用如下指令:
python train.py --task node_classification --dataset cora --model gcn
- 提交你的先进算法：如果您有一个性能优异的算法并愿意发布出来，你可以在我们的代码仓库里提出一个issue。在验证该算法的原创性，创造性和效果后，我们将该算法的效果添加到我们的排行榜上。
- 添加你自己的数据集：如果您有一个独特，有研究价值的数据集并且愿意发布出来，你可以在我们的代码仓库里提出一个issue，我们将把所以适合的模型在您的数据集上运行并更新我们的排行榜。
- 实现你自己的模型：如果您有一个性能优秀的算法，并愿意在我们的工具包中实现它，以帮助更多的人，您可以创建一个pull request，详细信息可见该页面。
如果您在我们的工具包或自定义步骤中遇到任何困难，请随时提出一个github issue或发表评论。您可以在24小时内得到答复。
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