BScNets: Block Simplicial Complex Neural Networks

Yuzhou Chen, Yulia R. Gel, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Simplicial neural networks (SNN) have recently emerged as the newest direction in graph learning which expands the idea of convolutional architectures from node space to simplicial complexes on graphs. Instead of pre-dominantly assessing pairwise relations among nodes as in the current practice, simplicial complexes allow us to describe higher-order interactions and multi-node graph structures. By building upon connection between the convolution operation and the new block Hodge-Laplacian, we propose the first SNN for link prediction. Our new Block Simplicial Complex Neural Networks (BScNets) model generalizes the existing graph convolutional network (GCN) frameworks by systematically incorporating salient interactions among multiple higher-order graph structures of different dimensions. We discuss theoretical foundations behind BScNets and illustrate its utility for link prediction on eight real-world and synthetic datasets. Our experiments indicate that BScNets outperforms the state-of-the-art models by a significant margin while maintaining low computation costs. Finally, we show utility of BScNets as the new promising alternative for tracking spread of infectious diseases such as COVID-19 and measuring the effectiveness of the healthcare risk mitigation strategies.

Original languageEnglish (US)
Title of host publicationAAAI-22 Technical Tracks 6
PublisherAssociation for the Advancement of Artificial Intelligence
Pages6333-6341
Number of pages9
ISBN (Electronic)1577358767, 9781577358763
StatePublished - Jun 30 2022
Event36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online
Duration: Feb 22 2022Mar 1 2022

Publication series

NameProceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022
Volume36

Conference

Conference36th AAAI Conference on Artificial Intelligence, AAAI 2022
CityVirtual, Online
Period2/22/223/1/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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