External Resources

  • Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu: Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting Paper, TensorFlow Code, PyTorch Code

  • Youngjoo Seo, Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst: Structured Sequence Modeling With Graph Convolutional Recurrent Networks Paper, Code

  • Jinyin Chen, Xuanheng Xu, Yangyang Wu, Haibin Zheng: GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction Paper

  • Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, Lujia Pan: Predicting Path Failure In Time-Evolving Graphs Paper, Code

  • Aynaz Taheri, Tanya Berger-Wolf: Predictive Temporal Embedding of Dynamic Graphs Paper

  • Aynaz Taheri, Kevin Gimpel, Tanya Berger-Wolf: Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models Paper, Code

  • 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, Code

  • Bing Yu, Haoteng Yin, Zhanxing Zhu: Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting Paper, Code