K-Core based Temporal Graph Convolutional Network for Dynamic Graphs

@article{Liu2022KCoreBT,
  title={K-Core based Temporal Graph Convolutional Network for Dynamic Graphs},
  author={Jingxin Liu and Chang Xu and Chang Yin and Weiqiang Wu and You Song},
  journal={IEEE Trans. Knowl. Data Eng.},
  year={2022},
  volume={34},
  pages={3841-3853}
}
  • Jingxin Liu, Chang Xu, You Song
  • Published 22 March 2020
  • Computer Science
  • IEEE Trans. Knowl. Data Eng.
Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information. However, many existing methods focus on static graphs while ignoring evolving graph patterns. Inspired by the success of graph convolutional networks(GCNs) in static graph embedding, we propose a novel k-core based temporal graph convolutional network, the CTGCN, to learn node representations for dynamic graphs. In… 
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