Simple Multigraph Convolution Networks
Danyang Wu Xinjie Shen Jitao Lu Jin Xu Feiping Nie
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.