Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data

Austin R. Benson; David F. Gleich; Desmond J. Higham

Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data

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arxiv 2103.05031 v1 pith:7YVRPKST submitted 2021-03-08 cs.SI physics.soc-ph

Austin R. Benson , David F. Gleich , Desmond J. Higham This is my paper · ORCID

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keywords ideasanalysisdatahigher-ordernetworkabstractionsacrossalgebra

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Higher-order network analysis uses the ideas of hypergraphs, simplicial complexes, multilinear and tensor algebra, and more, to study complex systems. These are by now well established mathematical abstractions. What's new is that the ideas can be tested and refined on the type of large-scale data arising in today's digital world. This research area therefore is making an impact across many applications. Here, we provide a brief history, guide, and survey.

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Enhancing the Utility of Higher-Order Information in Relational Learning
cs.LG 2025-02 conditional novelty 6.0 of 10

Graph-level GNNs with new hypergraph-based encodings beat hypergraph-specific GNNs on several benchmarks, and the encodings provably increase expressivity beyond graph-level encodings.

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