$(k,q)$-core decomposition of hypergraphs

TL;DR

This paper introduces a novel $(k,q)$-core decomposition method for hypergraphs to identify influential subgroups, analyzes its dynamics and phase transitions theoretically and numerically, and applies it to real-world data.

Contribution

It proposes a new $(k,q)$-core decomposition technique for hypergraphs, analyzes its pruning dynamics and phase transitions, and demonstrates its application to real datasets.

Findings

Pruning process exhibits a hybrid percolation transition for $k extgreater 2$ or $q extgreater 2$.

Critical exponents are confirmed through finite-size scaling analysis.

Degree-dependent critical relaxation dynamics are identified for $k=q=2$.

Abstract

In complex networks, many elements interact with each other in different ways. A hypergraph is a network in which group interactions occur among more than two elements. In this study, first, we propose a method to identify influential subgroups in hypergraphs, named $(k,q)$-core decomposition. The $(k,q)$-core is defined as the maximal subgraph in which each vertex has at least $k$ hypergraph degrees \textit{and} each hyperedge contains at least $q$ vertices. The method contains a repeated pruning process until reaching the $(k,q)$-core, which shares similarities with a widely used $k$-core decomposition technique in a graph. Second, we analyze the pruning dynamics and the percolation transition with theoretical and numerical methods in random hypergraphs. We set up evolution equations for the pruning process, and self-consistency equations for the percolation properties. Based on our theory, we find that the pruning process generates a hybrid percolation transition for either $k\ge 3$ \textit{or} $q\ge 3$. The critical exponents obtained theoretically are confirmed with finite-size scaling analysis. Next, when $k=q=2$, we obtain a unconventional degree-dependent critical relaxation dynamics analytically and numerically. Finally, we apply the $(k,q)$-core decomposition to a real coauthorship dataset and recognize the leading groups at an early stage.