Hypercore decomposition for non-fragile hyperedges: concepts, algorithms, observations, and applications

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Hypergraphs are a powerful abstraction for modeling high-order relations, which are ubiquitous in many fields. A hypergraph consists of nodes and hyperedges (i.e., subsets of nodes); and there have been a number of attempts to extend the notion of k-cores, which proved useful with numerous applications for pairwise graphs, to hypergraphs. However, the previous extensions are based on an unrealistic assumption that hyperedges are fragile, i.e., a high-order relation becomes obsolete as soon as a single member leaves it.In this work, we propose a new substructure model, called (k, t)-hypercore, based on the assumption that high-order relations remain as long as at least t fraction of the members remains. Specifically, it is defined as the maximal subhypergraph where (1) every node is contained in at least k hyperedges in it and (2) at least t fraction of the nodes remain in every hyperedge. We first prove that, given t (or k ), finding the (k,t)-hypercore for every possible k (or t ) can be computed in time linear w.r.t the sum of the sizes of hyperedges. Then, we demonstrate that real-world hypergraphs from the same domain share similar (k, t)-hypercore structures, which capture different perspectives depending on t. Lastly, we show the successful applications of our model in identifying influential nodes, dense substructures, and vulnerability in hypergraphs.
Publisher
SPRINGER
Issue Date
2023-11
Language
English
Article Type
Article
Citation

DATA MINING AND KNOWLEDGE DISCOVERY, v.37, no.6, pp.2389 - 2437

ISSN
1384-5810
DOI
10.1007/s10618-023-00956-2
URI
http://hdl.handle.net/10203/313761
Appears in Collection
AI-Journal Papers(저널논문)
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