External Resources - Architectures
==================================

* Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu: **Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting** `Paper <https://arxiv.org/abs/1707.01926>`_, `TensorFlow Code <https://github.com/liyaguang/DCRNN>`_, `PyTorch Code <https://github.com/chnsh/DCRNN_PyTorch>`_

* Youngjoo Seo, Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst: **Structured Sequence Modeling With Graph Convolutional Recurrent Networks** `Paper <https://arxiv.org/abs/1612.07659>`_, `Code <https://github.com/youngjoo-epfl/gconvRNN>`_, `TensorFlow Code <https://github.com/Zhangyang823/gconvRNN>`_ 

* Jinyin Chen, Xuanheng Xu, Yangyang Wu, Haibin Zheng: **GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction** `Paper <https://arxiv.org/abs/1812.04206>`_

* Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, Lujia Pan: **Predicting Path Failure In Time-Evolving Graphs** `Paper <https://arxiv.org/abs/1905.03994>`_, `Code <https://github.com/chocolates/Predicting-Path-Failure-In-Time-Evolving-Graphs>`_ 

* Aynaz Taheri, Tanya Berger-Wolf: **Predictive Temporal Embedding of Dynamic Graphs** `Paper <https://ieeexplore.ieee.org/document/9073186>`_

* Aynaz Taheri, Kevin Gimpel, Tanya Berger-Wolf: **Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models** `Paper <https://dl.acm.org/doi/10.1145/3308560.3316581>`_, `Code <https://github.com/CompBioUIC/RepLearning>`_ 

* Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, Charles E. Leiserson: **EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs** `Paper <https://arxiv.org/abs/1902.10191>`_, `Code <https://github.com/IBM/EvolveGCN>`_ 

* Bing Yu, Haoteng Yin, Zhanxing Zhu: **Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting** `Paper <https://arxiv.org/abs/1709.04875>`_, `Code <https://github.com/VeritasYin/STGCN_IJCAI-18>`_ 

* Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, Haifeng Li: **T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction** `Paper <https://arxiv.org/abs/1811.05320>`_, `Code <https://github.com/lehaifeng/T-GCN/T-GCN>`_

* Jiawei Zhu, Yujiao Song, Ling Zhao, Haifeng Li: **A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting** `Paper <https://arxiv.org/abs/2006.11583>`_, `Code <https://github.com/lehaifeng/T-GCN/tree/master/A3T-GCN>`_

* Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan: **Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting** `Paper <https://ojs.aaai.org/index.php/AAAI/article/view/3881>`_, `Code <https://github.com/guoshnBJTU/ASTGCN-r-pytorch>`_

* Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi: **GMAN: A Graph Multi-Attention Network for Traffic Prediction** `Paper <https://arxiv.org/pdf/1911.08415.pdf>`_, `Code <https://github.com/zhengchuanpan/GMAN>`_

* Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang: **Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks** `Paper <https://arxiv.org/abs/2005.11650>`_, `Code <https://github.com/nnzhan/MTGNN>`_

* Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang: **Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting** `Paper <https://arxiv.org/abs/2007.02842>`_, `Code <https://github.com/LeiBAI/AGCRN>`_

* George Panagopoulos, Giannis Nikolentzos, Michalis Vazirgiannis: **Transfer Graph Neural Networks for Pandemic Forecasting** `Paper <https://arxiv.org/abs/2009.08388>`_, `Code <https://github.com/geopanag/pandemic_tgnn>`_

* Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu: **Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition** `Paper <https://arxiv.org/abs/1805.07694>`_, `Code <https://github.com/lshiwjx/2s-AGCN>`_

* Le Yu, Leilei Sun, Bowen Du, Chuanren Liu, Hui Xiong, Weifeng Lv: **Predicting Temporal Sets with Deep Neural Networks** `Paper <https://dl.acm.org/doi/abs/10.1145/3394486.3403152>`_, `Code <https://github.com/yule-BUAA/DNNTSP>`_

External Resources - Datasets
=============================

* Benedek Rozemberczki, Paul Scherer, Oliver Kiss, Rik Sarkar, Tamas Ferenci: **Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks** `Paper <https://arxiv.org/abs/2102.08100>`_, `Dataset <https://github.com/benedekrozemberczki/spatiotemporal_datasets/>`_ 

* Ferenc Béres, Róbert Pálovics, Anna Oláh, András A. Benczúr: **Temporal Walk Based Centrality Metric for Graph Streams** `Paper <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214300/>`_, `Dataset <https://github.com/ferencberes/twittertennis>`_ 

* Ferenc Béres, Domokos M. Kelen, Róbert Pálovics, András A. Benczúr : **Node Embeddings in Dynamic Graphs** `Paper <https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0169-5>`_, `Dataset <https://github.com/ferencberes/twittertennis>`_ 

* Mallick, Tanwi and Balaprakash, Prasanna and Rask, Eric and Macfarlane, Jane: **Graph-partitioning-based diffusion convolutional recurrent neural network for large-scale traffic forecasting** `Paper <https://arxiv.org/abs/1909.11197>`_, `Dataset <https://github.com/tanwimallick/graph_partition_based_DCRNN/>`_