| 名称 | 类型 | 适用场景 | Github |
|---|---|---|---|
| OpenNE | 图表示学习 | 图节点表示学习，预训练 | https://github.com/thunlp/OpenNE |
| Graph_nets | 图神经网络 | 基于关系模糊的图数据推理 | https://github.com/deepmind/graph_nets |
| DGL | 图神经网络 | 建立图数据（可以无需通过networkx）并加载常用图神经网络 | https://github.com/jermainewang/dgl |
| GPF | 训练流程 | 基于关系数据的数据预测（节点分类、关系预测） | https://github.com/xchadesi/GPF |
| networkx | 图数据预处理 | 非大规模图数据预处理 | https://github.com/networkx/networkx |
| Euler | 工业级图深度学习框架 | 工业级图数据的用户研究快速进行算法创新与定制 | https://github.com/alibaba/euler |
| PyG | 几何深度学习 | 适合于图、点云、流形数据的深度学习，速度比DGL快 | https://github.com/rusty1s/pytorch_geometric |
| PBG | 图表示学习 | 高速大规模图嵌入工具,分布式图表示学习，使用pytorch | https://github.com/facebookresearch/PyTorch-BigGraph |
| AliGraph | 图神经网络 | 阿里自研，大规模图神经网络平台 | https://arxiv.org/pdf/1902.08730.pdf |
| NSL | 图神经网络 | 主要用来训练图神经网络 | https://www.tensorflow.org/neural_structured_learning/ |
| NGra | 图神经网络 | 支持图神经网络的并行处理框架 | https://arxiv.org/pdf/1810.08403.pdf |
| 关注点 | 类别 | 典型模型 | 引用 | Github |
|---|---|---|---|---|
| 图类型 | 无向 | GNN | ||
| 图类型 | 有向 | ADGPM | 《Rethinking knowledge graph propagation for zero-shot learning》 | https://github.com/cyvius96/adgpm |
| 图类型 | 异构图 | GraphInception | 《Deep collective classification in heterogeneous information networks》 | https://github.com/zyz282994112/GraphInception |
| 图类型 | 带有边信息的图 | G2S | 《 Graph-to-sequence learning using gated graph neural networks》 | https://github.com/beckdaniel/acl2018_graph2seq |
| 图类型 | 带有边信息的图 | RGCN | 《Modeling relational data with graph convolutionalnetworks》 | https://github.com/MichSchli/RelationPrediction / https://github.com/masakicktashiro/rgcn_pytorch_implementation 聚合更新 |
| 聚合更新 | 谱方法卷积聚合 | ChebNet | ||
| 聚合更新 | 非谱方法卷积聚合 | MoNet | ||
| 聚合更新 | 非谱方法卷积聚合 | DCNN | 《Diffusion-ConvolutionalNeural Networks》 | https://github.com/jcatw/dcnn |
| 聚合更新 | 非谱方法卷积聚合 | GraphSAGE | 《GraphSage: Representation Learning on Large Graphs》 | https://github.com/williamleif/GraphSAGE / https://github.com/williamleif/graphsage-simple |
| 聚合更新 | 注意力机制聚合 | GAT | 《Graph attention networks》 | https://github.com/PetarV-/GAT |
| 聚合更新 | 门更新机制 | GRU | 《Gated graphsequence neural networks》 | https://github.com/JamesChuanggg/ggnn.pytorch / https://github.com/yujiali/ggnn |
| 聚合更新 | 门更新机制 | LSTM | 《Improved semanticrepresentations from tree-structured long short-term memory networks》 | https://github.com/ttpro1995/TreeLSTMSentiment |
| 聚合更新 | 跳跃式连接 | Highway GNN | 《 Semi-supervised user geolocation via graph convolutional networks》 | https://github.com/afshinrahimi/geographconv |
| 聚合更新 | 跳跃式连接 | Jump Knowledge Network | 《Representation learning on graphs with jumping knowledge networks》 | |
| 训练方法 | 接受域的控制 | |||
| 训练方法 | 采样方法 | FastGCN | 《FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling》 | https://github.com/matenure/FastGCN |
| 训练方法 | 梯度提升方法 | Co-Training GCN | ||
| 训练方法 | 梯度提升方法 | Self-training GCN | ||
| 通用框架 | 信息传播 | MPNN | 《Neural message passing for quantum chemistry》 | https://github.com/brain-research/mpnn |
| 通用框架 | 非局部神经网络 | NLNN | 《 Non-local neuralnetworks》 | https://github.com/nnUyi/Non-Local_Nets-Tensorflow / https://github.com/search?q=Non-local+neural+networks |
| 通用框架 | 图神经网络 | GN | 《Relational inductive biases, deep learning, andgraph networks》 | https://github.com/deepmind/graph_nets |
| 领域 | 应用 | 算法 | 引用 | Github |
|---|---|---|---|---|
| 通用 | 关系预测 | RGCN | 《Modeling Relational Data with Graph Convolutional Networks》 | rgcn_pytorch_implementation |
| 通用 | 关系预测 | SEAL | 《Link Prediction Based on Graph Neural Networks》 | SEAL |
| 通用 | 节点分类 | |||
| 通用 | 社区检测 | 《Improved Community Detection using Deep Embeddings from Multilayer Graphs》 | ||
| 通用 | 社区检测 | Hierarchical GNN | 《Supervised Community Detection with Hierarchical Graph Neural Networks》 | |
| 通用 | 图分类 | 《Graph Classification using Structural Attention》 | ||
| 通用 | 图分类 | DGCNN | 《An End-to-End Deep Learning Architecture for Graph Classification》 | pytorch_DGCNN |
| 通用 | 推荐 | GCN | 《Graph Convolutional Neural Networks for Web-Scale Recommender Systems》 | |
| 通用 | 图生成 | NetGAN | 《 Net-gan: Generating graphs via random walks》 | |
| 通用 | 图生成 | GraphRNN | 《GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models》 | |
| 通用 | 图生成 | MolGAN | 《 Molgan: An implicit generative model for small molecular graphs》 | |
| 决策优化 | 旅行商问题 | GNN | 《Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP》《Attention solves your tsp》 | https://github.com/machine-reasoning-ufrgs/TSP-GNN https://github.com/wouterkool/attention-tsp |
| 决策优化 | 规划器调度 | GNN | 《Adaptive Planner Scheduling with Graph Neural Networks》《Revised note on learning quadratic assignment with graph neural networks》 | |
| 决策优化 | 组合优化 | GCN structure2vec | 《Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search》《 Learning combinatorial optimization algorithms over graphs》 | NPHard |
| 交通 | 出租车需求预测 | 《Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction》 | DMVST-Net | |
| 交通 | 交通流量预测 | 《Spatio-Temporal Graph Convolutional Networks:A Deep Learning Framework for Traffic Forecasting》 | STGCN-PyTorch | |
| 交通 | 交通流量预测 | 《DIFFUSION CONVOLUTIONAL RECURRENT NEURAL NETWORK: DATA-DRIVEN TRAFFIC FORECASTING》 | DCRNN | |
| 传感网络 | 传感器布局 | 《Distributed Graph Layout for Sensor Networks》 | ||
| 区域安全 | 疾病传播 | 《Predicting and controlling infectious disease epidemics using temporal networks》 | ||
| 区域安全 | 城市人流预测 | 《FCCF: Forecasting Citywide Crowd Flows Based on Big Data》 | ||
| 社交网络 | 影响力预测 | GCN/GAT | 《DeepInf: Social Influence Prediction with Deep Learning》 | DeepInf |
| 社交网络 | 转发动作预测 | 《Social Influence Locality for Modeling Retweeting Behaviors》 | ||
| 社交网络 | 转发动作预测 | 《 Predicting Retweet via Social Influence Locality》 | ||
| 文本 | 文本分类 | GCN | "《Diffusion-convolutional neural networks》《 Convolutionalneural networks on graphs with fast localized spectral filtering》《Knowledgetransfer for out-of-knowledge-base entities : A graph neuralnetwork approach》《 Deep convolutional networks on graph-structured data》《 Semi-supervised classification with graph convolutional networks》《 Geometric deep learning on graphs and manifolds using mixture model cnns》" | dcnn-tensorflow |
| 文本 | 文本分类 | GAT | 《Graph attention networks》 | |
| 文本 | 文本分类 | DGCNN | 《Large-scale hierarchical text classification with recursively regularized deep graph-cnn》 | DeepGraphCNNforTexts |
| 文本 | 文本分类 | Text GCN | 《Graph convolutional networks for text classification》 | text_gcn |
| 文本 | 文本分类 | Sentence LSTM | 《 Sentence-state LSTM for text representation》 | S-LSTM |
| 文本 | 序列标注(POS, NER) Sentence LSTM | 《 Sentence-state LSTM for textrepresentation》 | https://github.com/leuchine/S-LSTM | |
| 文本 | 语义分类 | LSTM | 《 Improved semantic representations from tree-structured long short-term memorynetworks》 | https://github.com/ttpro1995/TreeLSTMSentiment |
| 文本 | 语义角色标注Syntactic | GCN | 《Encoding sentences with graph convolutional networks for semantic role labeling》 | |
| 文本 | 机器翻译 | GCN | 《Graph convolutional encoders for syntax-aware neural machine translation》/《 Exploiting semantics in neural machine translation with graph convolutional networks》" | |
| 文本 | 机器翻译 | GGNN | 《 Graph-to-sequence learningusing gated graph neural networks. 》 | https://github.com/beckdaniel/acl2018_graph2seq |
| 文本 | 关系抽取 | LSTM | 《 End-to-end relation extraction usinglstms on sequences and tree structures》 | |
| 文本 | 关系抽取 | Graph LSTM | 《Crosssentencen-ary relation extraction with graph lstms》/《 N-ary relationextraction using graph state lstm》 | https://github.com/freesunshine0316/nary-grn |
| 文本 | 关系抽取 | GCN | 《 Graph convolution over pruned dependency trees improves relation extraction》 | https://github.com/qipeng/gcn-over-pruned-trees |
| 文本 | 事件抽取 | GCN | 《 Jointly multiple events extractionvia attention-based graph information aggregation》/《. Graph convolutional networks with argument-aware pooling for event detection》 | https://github.com/lx865712528/JMEE |
| 文本 | 文本生成 | Sentence LSTM | 《A graph-to-sequence mdel for amr-to-text generation》 | |
| 文本 | 文本生成 | GGNN | 《 Graph-to-sequence learningusing gated graph neural networks》 | |
| 文本 | 阅读理解 | Sentence LSTM | 《Exploring graph-structured passage representation for multihop reading comprehension with graph neural networks》 | |
| 图像/视频 | 社会关系理解 | GRM | 《Deep reasoning with knowledge graph for social relationship understanding》 | https://github.com/wzhouxiff/SR |
| 图像/视频 | 图像分类 | GCN | 《 Few-shot learning with graph neuralnetworks》/《Zero-shot recognition via semantic embeddings and knowledge graphs》 | https://github.com/louis2889184/gnn_few_shot_cifar100 https://github.com/JudyYe/zero-shot-gcn |
| 图像/视频 | 图像分类 | GGNN | 《 Multi-label zero-shot learning with structured knowledge graphs》 | https://people.csail.mit.edu/weifang/project/vll18-mlzsl/ |
| 图像/视频 | 图像分类 | ADGPM | 《Rethinking knowledge graph propagation for zero-shot learning》 | https://github.com/cyvius96/adgpm |
| 图像/视频 | 图像分类 | GSNN | 《The more you know: Using knowledge graphs for image classification》 | https://github.com/KMarino/GSNN_TMYN |
| 图像/视频 | 视觉问答 | GGNN | 《Graph-structured representations for visual question answering》/《Deep reasoning with knowledge graph for social relationship understanding》 " | |
| 图像/视频 | 领域识别 | GCNN | 《Iterative visual reasoning beyond convolutions》 | https://github.com/coderSkyChen/Iterative-Visual-Reasoning.pytorch |
| 图像/视频 | 语义分割 | Graph LSTM | 《 Interpretablestructure-evolving lstm》《 Semantic objectparsing with graph lstm》 | |
| 图像/视频 | 语义分割 | GGNN | 《Large-scale point cloud semantic segmentation with superpoint graphs》 | https://github.com/loicland/superpoint_graph |
| 图像/视频 | 语义分割 | DGCNN | 《Dynamic graph cnn for learning on point clouds》 | https://github.com/af13s/dgcnn-amino |
| 图像/视频 | 语义分割 | 3DGNN | 《 3d graph neural networks for rgbd semantic segmentation》 | https://github.com/yanx27/3DGNN_pytorch |
| 生物科技 | 物理系统 | IN | 《 Interaction networks for learning about objects, relations and physics》 | https://github.com/higgsfield/interaction_network_pytorch https://github.com/jaesik817/Interaction-networks_tensorflow |
| 生物科技 | 物理系统 | VIN | 《 Visual interaction networks: Learning a physics simulator from video》 | |
| 生物科技 | 物理系统 | GN | 《 Graph networks as learnable physics engines for inference and control》 | https://github.com/fxia22/gn.pytorch |
| 生物科技 | 分子指纹 | GCN | 《Convolutional networks on graphs for learning molecular fingerprints》 | https://github.com/fllinares/neural_fingerprints_tf |
| 生物科技 | 分子指纹 | NGF | 《Molecular graph convolutions: moving beyond fingerprints》 | |
| 生物科技 | 蛋白质界面预测 | GCN | 《Protein interfaceprediction using graph convolutional networks》 | https://github.com/fouticus/pipgcn |
| 生物科技 | 药物副作用预测 | Decagon | 《Modeling polypharmacyside effects with graph convolutional networks》 | https://github.com/miliana/DecagonPython3 |
| 生物科技 | 疾病分类 | PPIN | 《Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification》 |