Policy-GNN: Aggregation Optimization for Graph Neural Networks

@article{Lai2020PolicyGNNAO,
  title={Policy-GNN: Aggregation Optimization for Graph Neural Networks},
  author={Kwei-Herng Lai and D. Zha and K. Zhou and X. Hu},
  journal={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
  year={2020}
}
  • Kwei-Herng Lai, D. Zha, +1 author X. Hu
  • Published 2020
  • Computer Science, Mathematics
  • Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Graph data are pervasive in many real-world applications. Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors with stackable network modules. Motivated by the observation that different nodes often require different iterations of aggregation to fully capture the structural information, in this paper, we propose to explicitly sample diverse… Expand
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