[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.