awesome-gcn

This repository is to collect GCN, GAT(graph attention) related resources.

Github Repositories:

Implement:

1. tkipf/gcn, Implementation of Graph Convolutional Networks in TensorFlow,

2. tkipf/keras-gcn, Keras implementation of Graph Convolutional Networks,

3. OCEChain/GCN, Graph Convolutional Networks,

4. PetarV-/GAT, Graph Attention Networks (https://arxiv.org/abs/1710.10903),

5. Diego999/pyGAT, Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903),

6. mp2893/gram, Graph-based Attention Model,

7. danielegrattarola/keras-gat, Keras implementation of the graph attention networks (GAT) by Veličković et al. (2017; https://arxiv.org/abs/1710.10903),

8. Luckick/EAGCN, Implementation of Edge Attention based Multi-relational Graph Convolutional Networks,

Improved GCN:

1. lightaime/deep_gcns, Repo for “Can GCNs Go as Deep as CNNs?”,

Example & Tutorial:

1. dbusbridge/gcn_tutorial, A tutorial on Graph Convolutional Neural Networks,

Knowledge Graph:

1. tkipf/relational-gcn, Keras-based implementation of Relational Graph Convolutional Networks

2. 1049451037/GCN-Align, Code of the paper: Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks.

3. MichSchli/RelationPrediction, Implementation of R-GCNs for Relational Link Prediction

4. xiangwang1223/knowledge_graph_attention_network, KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019,

5. deepakn97/relationPrediction, ACL 2019: Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs,

Relation Extraction:

1. qipeng/gcn-over-pruned-trees, Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (authors’ PyTorch implementation),

2. malllabiisc/RESIDE, EMNLP 2018: RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information,

3. Cartus/AGGCN_TACRED, Attention Guided Graph Convolutional Networks for Relation Extraction (authors’ PyTorch implementation for the ACL19 paper),

Text Classification:

1. yao8839836/text_gcn, Graph Convolutional Networks for Text Classification. AAAI 2019,

2. yuanluo/text_gcn_tutorial, This tutorial (currently under development) is based on the implementation of Text GCN in our paper: Liang Yao, Chengsheng Mao, Yuan Luo. “Graph Convolutional Networks for Text Classification.” In 33rd AAAI Conference on Artificial Intelligence (AAAI-19),

3. plkmo/Bible_Text_GCN, Text-Based Graph Convolution Network,

4. iamjagdeesh/Fake-News-Detection, Fake news detector based on the content and users associated with it using BERT and Graph Attention Networks (GAT).,

Word Embedding:

3. malllabiisc/WordGCN, ACL 2019: Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks,

NER:

1. ContextScout/gcn_ner, Graph Convolutional neural network named entity recognition,

QA:

1. berc-uoft/Transformer-GCN-QA, A multi-hop Q/A architecture based on transformers and GCNs,

Coreference Resolution:

1. ianycxu/RGCN-with-BERT, Graph Convolutional Networks (GCN) with BERT for Coreference Resolution Task [Pytorch][DGL],

Recommendation:

1. PeiJieSun/diffnet, This code is released for the paper: Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang and Meng Wang. A Neural Influence Diffusion Model for Social Recommendation. Accepted by SIGIR2019.

Skeleton-Based Action Recognition:

1. yysijie/st-gcn, Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch

Anomaly Detection:

1. jx-zhong-for-academic-purpose/GCN-Anomaly-Detection, Placeholder of the source codes in CVPR 2019: Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection.

2. kaize0409/GCN_AnomalyDetection, Code for Deep Anomaly Detection on Attributed Networks (SDM2019).

Face Clustering:

1. Zhongdao/gcn_clustering, Code for CVPR’19 paper Linkage-based Face Clustering via GCN,

2. yl-1993/learn-to-cluster, Learning to Cluster Faces on an Affinity Graph (CVPR 2019),

Person Attribute Recognition:

2. 2014gaokao/pedestrian-attribute-recognition-with-GCN, GCN for pedestrian attribute recognition in surveillance scenarios,

Person Search:

1. sjtuzq/person_search_gcn, This repository hosts the code for our paper “Learning Context Graph for Person Search”, CVPR2019 Oral,

Image Segmentation:

1. fidler-lab/curve-gcn, Official PyTorch code for Curve-GCN (CVPR 2019),

Image Classification:

1. chenzhaomin123/ML_GCN, PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019,

2. rusty1s/graph-based-image-classification,Implementation of Planar Graph Convolutional Networks in TensorFlow,

3. avirambh/MSDNet-GCN,ICLR 2018 reproducibility challenge - Multi-Scale Dense Convolutional Networks for Efficient Prediction,

4. JudyYe/zero-shot-gcn,Zero-Shot Learning with GCN (CVPR 2018),

Scene Graph Generation:

1. NVIDIA/ContrastiveLosses4VRD,Implementation for the CVPR2019 paper “Graphical Contrastive Losses for Scene Graph Generation”,

2. yuweihao/KERN,Code for Knowledge-Embedded Routing Network for Scene Graph Generation (CVPR 2019),

3. shijx12/XNM-Net,Pytorch implementation of "Explainable and Explicit Visual Reasoning over Scene Graphs ",

4. jiayan97/linknet-pytorch,Pytorch reimplementation of LinkNet for Scene Graph Generation,

5. Uehwan/3D-Scene-Graph,3D scene graph generator implemented in Pytorch.,

6. Kenneth-Wong/sceneGraph_Mem,Codes for CVPR 2019: Exploring Context and Visual Pattern of Relationship for Scene Graph Generation, Wenbin Wang, Ruiping Wang, Shiguang Shan, Xilin Chen, CVPR 2019.,

7. danfeiX/scene-graph-TF-release,“Scene Graph Generation by Iterative Message Passing” code repository http://cs.stanford.edu/~danfei/scene-…,

8. google/sg2im,Code for “Image Generation from Scene Graphs”, Johnson et al, CVPR 2018,

9. rowanz/neural-motifs,Code for Neural Motifs: Scene Graph Parsing with Global Context (CVPR 2018) https://rowanzellers.com/neuralmotifs,

10. jwyang/graph-rcnn.pytorch,Pytorch code for our ECCV 2018 paper “Graph R-CNN for Scene Graph Generation” and other papers,

11. yikang-li/FactorizableNet, Factorizable Net (Multi-GPU version): An Efficient Subgraph-based Framework for Scene Graph Generation,

Traffic Flow:

1. lehaifeng/T-GCN, Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method

2. FrancesZhou/GCNTrafficPrediction,

3. Davidham3/ASTGCN, Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (ASTGCN) AAAI 2019,

Disease Prediction:

2. parisots/population-gcn,Graph CNNs for population graphs: classification of the ABIDE dataset,

Path Prediction:

1. Zhenye-Na/gcn-spp, Shortest Path prediction using Graph Convolutional Networks,

2. raphaelavalos/attention_tsp_graph_net, Implementation of Attention Solves Your TSP, Approximately (W. Kool et al.) with the DeepMind’s Graph Nets library,

3D Point Cloud:

1. maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification, Code for A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION (ICASSP 2018),

2. jiexiong2016/GCNv2_SLAM, Real-time SLAM system with deep features,

Graph To Sequence:

1. wngzhiqi/Graph2Seq-Graph-to-Sequence-Learning-with-Attention-Based-Neural-Networks, This repo is project for 11785 (Deep Learning) at CMU. We are reproducing paper called “Graph2Seq: Graph to Sequence Learning with Attention-Based Neural Networks”(https://arxiv.org/pdf/1804.00823.pdf). Team Member: Zhiqi Wang, Ziyin Huang, Hong Du, Zhengkai Zhang,

2. syxu828/Graph2Seq-0.1, This is the code for paper “Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks”,

Chemical Stability Prediction:

1. MingCPU/DeepChemStable, DeepChemStable: chemical stability prediction using attention-based graph convolution network,