Skip to content

MightyCrane/GNN

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

40 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

GraphNeuralNetwork

The Tools of the GraphNeuralNetwork

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

The Method of Build GNN Model

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

The Application of the GraphNeuralNetwork

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》

About

The learning of the GraphNeuralNetwork

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published