Simple Multigraph Convolution Networks
<|reference_start|>Simple Multigraph Convolution Networks: 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.<|reference_end|>
@article{wu2024simple,
    title={Simple Multigraph Convolution Networks},
    author={Danyang Wu, Xinjie Shen, Jitao Lu, Jin Xu, Feiping Nie},
    journal={arXiv preprint arXiv:2403.05014},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05014},
    primaryClass={cs.LG cs.AI}
}