Simple Multigraph Convolution Networks

• URL: http://arxiv.org/abs/2403.05014v1
• Date: Fri, 8 Mar 2024 03:27:58 GMT
• Title: Simple Multigraph Convolution Networks
• Authors: Danyang Wu, Xinjie Shen, Jitao Lu, Jin Xu, Feiping Nie
• Abstract summary: Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view operators. This paper proposes a Simple Multi Convolution Networks (SMGCN) which first extracts consistent cross-view topology from multigraphs including edge-level and subgraph-level topology, then performs expansion based on raw multigraphs and consistent topologies. In theory, SMGCN utilizes the consistent topologies in expansion rather than standard cross-view expansion, which performs credible cross-view spatial message-passing, and effectively reduces the complexity of standard expansion.
• Score: 49.19906483875984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators. To alleviate this problem, this paper proposes a Simple MultiGraph Convolution Networks (SMGCN) which first extracts consistent cross-view topology from multigraphs including edge-level and subgraph-level topology, then performs polynomial expansion based on raw multigraphs and consistent topologies. In theory, SMGCN utilizes the consistent topologies in polynomial expansion rather than standard cross-view polynomial expansion, which performs credible cross-view spatial message-passing, follows the spectral convolution paradigm, and effectively reduces the complexity of standard polynomial expansion. In the simulations, experimental results demonstrate that SMGCN achieves state-of-the-art performance on ACM and DBLP multigraph benchmark datasets. Our codes are available at https://github.com/frinkleko/SMGCN.

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