This page is to summarize important materials about dynamic (temporal) embedding.

WWW Learning Temporal Interaction Graph Embedding via Coupled Memory Networks

https://dl.acm.org/doi/pdf/10.1145/3366423.3380076

WWW Continuous-Time Link Prediction via Temporal DependentGraph Neural Network

https://dl.acm.org/doi/pdf/10.1145/3366423.3380073

K-Core based Temporal Graph Convolutional Network for Dynamic Graphs

https://arxiv.org/abs/2003.09902

• Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey

• Representation Learning for Dynamic Graphs: A Survey

• TGAT: INDUCTIVE REPRESENTATION LEARNING ON TEMPORAL GRAPHS
  • ICLR2020
• DyREP: Learning Representations over Dynamic Graphs (Extrapolation)
  • Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha. ICLR 2019.
• DynGEM: Deep Embedding Method for Dynamic Graphs
  • Palash Goyal, Nitin Kamra, Xinran He, Yan Liu. IJCAI 2017.
• Graph2Seq: Scalable Learning Dynamics for Graphs
  • Shaileshh Bojja Venkatakrishnan, Mohammad Alizadeh, Pramod Viswanath
• Dynamic Graph Representation Learning via Self-Attention Networks
  • WSDM2020 Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, Hao Yang
• Continuous-Time Dynamic Network Embeddings
  • WWW 2018. Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim.
• [Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat]
  • KDD 2019
• GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction
  • Jinyin Chen, Xuanheng Xu, Yangyang Wu, Haibin Zheng
• Learning Dynamic Embeddings from Temporal Interaction Networks
  • Srijan Kumar, Xikun Zhang, Jure Leskovec
• Dynamic Graph Convolutional Networks
  • Franco Manessi, Alessandro Rozza, Mario Manzo
• Streaming Graph Neural Networks
  • Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin

• Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding
  • Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang
• EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
  • Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Charles E. Leisersen, AAAI2020.
• Gated Residual Recurrent Graph Neural Networks for Traffic Prediction
  • Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng, AAAI 2019.
• Structured Sequence Modeling with Graph Convolutional Recurrent Networks
  • Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson, ICONIP 2017.
• Dynamic Network Embedding by Modeling Triadic Closure Process
  • Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang. AAAI 2018.

• KDD'18 NetWalk: A flexible deep embedding approach for anomaly detection in dynamic networks
• IJCAI'19 AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN

• Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
• Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method
• AAAI2019 Gated Residual Recurrent Graph Neural Networks for Traffic Prediction

• Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

• G-TAD: Sub-Graph Localization for Temporal Action Detection

• Link prediction
• Change detection
• Graph reconstruction

• Temporal Convolutional Networks: A Unified Approach to Action Segmentation
  • Colin Lea, Rene Vidal, Austin Reiter, Gregory D. Hager
• What to Do Next: Modeling User Behaviors by Time-LSTM
  • Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, Deng Cai. IJCAI 2017.
• Patient Subtyping via Time-Aware LSTM Networks
  • Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou. KDD 2017.