Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks

  title={Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks},
  author={Kuangqi Zhou and Yanfei Dong and Kaixin Wang and Wee Sun Lee and Bryan Hooi and Huan Xu and Jiashi Feng},
  journal={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  • Kuangqi ZhouYanfei Dong Jiashi Feng
  • Published 12 June 2020
  • Computer Science
  • Proceedings of the 30th ACM International Conference on Information & Knowledge Management
A Graph Convolutional Network (GCN) stacks several layers and in each layer performs a PROPagation operation~(PROP) and a TRANsformation operation~(TRAN) for learning node representations over graph-structured data. Though powerful, GCNs tend to suffer performance drop when the model gets deep. Previous works focus on PROPs to study and mitigate this issue, but the role of TRANs is barely investigated. In this work, we study performance degradation of GCNs by experimentally examining howโ€ฆย 

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