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[00664] A New Sampling Technique for Learning with Hypergraph Neural Networks

  • Session Time & Room : 3C (Aug.23, 13:20-15:00) @E811
  • Type : Contributed Talk
  • Abstract : Hypergraphs can represent higher-order relations among objects. Traditional hypergraph neural networks produce high computational cost and timing. We propose a new sampling technique for learning with hypergraph neural networks. The core idea is to design a layer-wise sampling scheme for nodes and hyperedges to approximate original hypergraph convolution. Notably, the proposed sampling technique allows us to handle large-scale hypergraph learning. Experiment results demonstrate that our proposed model keeps a good balance between time and accuracy.
  • Classification : 68T07, 05C65, 62D05, 68T09, large-scale hypergraph learning, hypergraph neural networks, hypergraph sampling, variance reduction, importance sampling
  • Format : Talk at Waseda University
  • Author(s) :
    • Fengcheng Lu (The University of Hong Kong)
    • Michael Kwok-Po Ng (The University of Hong Kong)