Efficient hypergraph neural network on million-level data
High-order correlations are ubiquitous in the real world,such as the social network,the biological network,and the transportation network.It is of significant importance to model and optimize high-order correlations for network investigation.The hypergraph,as a flexible and scalable structure,can be applied to model the high-order correlations in a natural manner.With the development of deep learning,hypergraph neural networks(HGNNs)are widely leveraged for high-order correlation modeling and optimization.Although existing HGNNs have shown decent performance on small-scale datasets,they cannot be applied to large-scale data due to their expensive space cost caused by the transductive learning paradigm in that case.This paper first analyzes the root causes of the deficiency that HGNNs are unable to handle large-scale data.Furthermore,this paper presents the efficient hypergraph neural network(EHGNN)towards the million-level data.EHGNN designs the hypergraph sampling module and the computational acceleration module that is based on single-stage hypergraph convolution,reducing the time and space cost of HGNNs.Experimental results on four real-world hypergraph datasets demonstrate the effectiveness and efficiency of the proposed EHGNN.
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