hyper-VDrank ranks nodes via higher-order competition dynamics and hyperedge weights to dismantle hypergraphs faster under strong deletion, improving efficiency 23.65% and lowering collapse threshold 27.63% on 14 real hypergraphs versus baselines.
Subhypergraphs in non-uniform random hypergraphs
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
In this paper we focus on the problem of finding (small) subhypergraphs in a (large) hypergraph. We use this problem to illustrate that reducing hypergraph problems to graph problems by working with the 2-section is not always a reasonable approach. We begin by defining a generalization of the binomial random graph model to hypergraphs and formalizing several definitions of subhypergraph. The bulk of the paper focusses on determining the expected existence of these types of subhypergraph in random hypergraphs. We also touch on the problem of determining whether a given subgraph appearing in the 2-section is likely to have been induced by a certain subhypergraph in the hypergraph. To evaluate the model in relation to real-world data, we compare model prediction to two datasets with respect to (1) the existence of certain small subhypergraphs, and (2) a clustering coefficient.
fields
physics.soc-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Identifying vulnerable nodes for hypergraph dismantling via higher-order competition dynamics
hyper-VDrank ranks nodes via higher-order competition dynamics and hyperedge weights to dismantle hypergraphs faster under strong deletion, improving efficiency 23.65% and lowering collapse threshold 27.63% on 14 real hypergraphs versus baselines.