Phuc Hao Do

Phuc Hao Do

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[Note] Hiểu hơn về Graph network

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Hiểu hơn về graph network

Một số paper phổ biến

Computer Network

  1. Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN. ACM SOSR 2019. paper

    Krzysztof Rusek, José Suárez-Varela, Albert Mestres, Pere Barlet-Ros, Albert Cabellos-Aparicio.

Traffic Network

  1. Spatiotemporal Multi‐Graph Convolution Network for Ride-hailing Demand Forecasting. AAAI 2019. paper

    Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, Yan Liu.

  2. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. AAAI 2019. paper

    Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan.

  3. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. arxiv 2018. paper

    Zhiyong Cui, Kristian Henrickson, Ruimin Ke, Yinhai Wang.

  4. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. IJCAI 2018. paper

    Bing Yu, Haoteng Yin, Zhanxing Zhu.

  5. Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling. KDD 2019. paper

    Yuandong Wang, Hongzhi Yin, Hongxu Chen, Tianyu Wo, Jie Xu, Kai Zheng.

  6. Predicting Path Failure In Time-Evolving Graphs. KDD 2019. paper

    Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, Lujia Pan.

  7. Stochastic Weight Completion for Road Networks using Graph Convolutional Networks. ICDE 2019. paper

    Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen.

  8. STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting. IJCAI 2019. paper

    Lei Bai, Lina Yao, Salil.S Kanhere, Xianzhi Wang, Quan.Z Sheng.

  9. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. IJCAI 2019. paper

    Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang.

  10. Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction. AAAI 2020. paper

    Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, Hui Xiong.

  11. Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks. NeurIPS 2019. paper

    Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, Hamid Rezatofighi, Silvio Savarese.

  12. GMAN: A Graph Multi-Attention Network for Traffic Prediction. AAAI 2020. paper

    Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi.

Reinforcement Learning

  1. NerveNet: Learning Structured Policy with Graph Neural Networks. ICLR 2018. paper

    Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler.

  2. Structured Dialogue Policy with Graph Neural Networks. ICCL 2018. paper

    Lu Chen, Bowen Tan, Sishan Long, Kai Yu.

  3. Action Schema Networks: Generalised Policies with Deep Learning. AAAI 2018. paper

    Sam Toyer, Felipe Trevizan, Sylvie Thiébaux, Lexing Xie.

  4. Relational inductive bias for physical construction in humans and machines. CogSci 2018. paper

    Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Joshua B. Tenenbaum, Peter W. Battaglia.

  5. Relational Deep Reinforcement Learning. arxiv 2018. paper

    Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, Peter Battaglia.

  6. Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning. NAACL 2019. paper

    Prithviraj Ammanabrolu, Mark O. Riedl.

  7. Learning Transferable Graph Exploration. NeurIPS 2019. paper

    Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli.

  8. Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments. IV 2020. paper

    Patrick Hart, Alois Knoll.

  9. Multi-Agent Game Abstraction via Graph Attention Neural Network. AAAI 2020. paper

    Yong Liu, Weixun Wang, Yujing Hu, Jianye Hao, Xingguo Chen, Yang Gao.

  10. Graph Convolutional Reinforcement Learning. ICLR 2020. paper

    Jiechuan Jiang, Chen Dun, Tiejun Huang, Zongqing Lu.

  11. Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation. ICLR 2020. paper

    Yu Chen, Lingfei Wu, Mohammed J. Zaki.

  12. Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs. ICLR 2020. paper

    Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, Oriol Vinyals.

Link ref:

https://mlabonne.github.io/blog/gat/
https://paperswithcode.com/task/graph-classification
https://github.com/jwwthu/GNN4Traffic
https://github.com/jwwthu/GNN-Communication-Networks
https://github.com/jmhIcoding/fgnet
federated learning into graph
https://github.com/huweibo/Awesome-Federated-Learning-on-Graph-and-GNN-papers

https://github.com/gorgen2020/SDGCN/tree/main/SDGCN
https://github.com/YanJieWen/STGMT-Tensorflow-implementation
https://github.com/wengwenchao123/DDGCRN
https://github.com/kaist-dmlab/MG-TAR
https://github.com/Bounger2/ST-CGCN
https://github.com/kaist-dmlab/MG-TAR
https://github.com/tsinghua-fib-lab/Traffic-Benchmark
https://github.com/csyanghan/PGECRN
https://github.com/346644054/ST-3DGMR
https://github.com/MathiasNT/NRI_for_Transport
https://github.com/ZikangZhou/QCNet
https://github.com/LMissher/STWave
https://github.com/HKUDS/AutoST
https://github.com/deepkashiwa20/MegaCRN
https://github.com/Echo-Ji/ST-SSL
https://github.com/zhengdaoli/AGC-net
https://github.com/trainingl/STG4Traffic
https://github.com/liuxu77/LargeST
https://github.com/jdcaicedo251/transit_demand_prediction
https://github.com/jwwthu/GNN4Traffic

https://github.com/newlei/FairGo
https://github.com/joeybose/Flexible-Fairness-Constraints
https://github.com/akaxlh/KHGT
https://github.com/tsinghua-fib-lab/MBGCN
https://github.com/WHUIR/PPGN
https://github.com/twchen/SEFrame
https://github.com/xiaxin1998/COTREC
https://github.com/RUCAIBox/RecBole/blob/master/recbole/model/sequential_recommender/gcsan.py
https://github.com/userbehavioranalysis/SR-GNN_PyTorch-Geometric
https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/gnn/
https://github.com/tsinghua-fib-lab/SIGIR21-SURGE
https://github.com/retagnn/RetaGNN
https://github.com/zhuty16/GES
https://github.com/Coder-Yu/QRec
https://github.com/lcwy220/Social-Recommendation
https://github.com/Wang-Shuo/GraphRec_PyTorch
https://github.com/wenqifan03/GraphRec-WWW19
https://github.com/Kanika91/diffnet
https://github.com/PeiJieSun/diffnet
https://github.com/Wenhui-Yu/LCFN
https://github.com/hanliu95/HS-GCN
https://github.com/jeongwhanchoi/HMLET
https://github.com/wujcan/SGL-TensorFlow
https://github.com/liufancs/IMP_GCN
https://github.com/Tingting2477/DGCF_torch
https://github.com/xiangwang1223/disentangled_graph_collaborative_filtering
https://github.com/gusye1234/LightGCN-PyTorch
https://github.com/gusye1234/LightGCN-PyTorch
https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems

https://github.com/ailabteam/MAppGraph/blob/gh-pages/mappgraph/notebooks/train_GNN.ipynb

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