no code implementations • 7 Dec 2022 • Jiarui Sun, Mengting Gu, Chin-Chia Michael Yeh, Yujie Fan, Girish Chowdhary, Wei zhang
Node classification on dynamic graphs is challenging for two reasons.
no code implementations • 11 Aug 2022 • Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei zhang
Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer.
no code implementations • 19 Nov 2021 • Weilin Cong, Yanhong Wu, Yuandong Tian, Mengting Gu, Yinglong Xia, Chun-cheng Jason Chen, Mehrdad Mahdavi
To achieve efficient and scalable training, we propose temporal-union graph structure and its associated subgraph-based node sampling strategy.
no code implementations • 29 Sep 2021 • Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei zhang
When applying such type of networks on graph without node feature, one can extract simple graph-based node features (e. g., number of degrees) or learn the input node representation (i. e., embeddings) when training the network.
no code implementations • 15 Aug 2021 • Yuhang Wu, Mengting Gu, Lan Wang, Yusan Lin, Fei Wang, Hao Yang
Modeling inter-dependencies between time-series is the key to achieve high performance in anomaly detection for multivariate time-series data.