Uncovering High-Order Cohesive Structures: Efficient (k,g)-Core Computation and Decomposition for Large Hypergraphs

TL;DR

This paper introduces an efficient indexing method for discovering high-order cohesive structures in large hypergraphs, enabling faster online retrieval and analysis of complex relationships.

Contribution

It presents a novel indexing structure for (k,g)-core computation in hypergraphs, improving efficiency and scalability for cohesive subgraph discovery.

Findings

Outperforms existing methods in speed and accuracy

Enables real-time analysis of large hypergraphs

Demonstrated effectiveness on real-world network data

Abstract

Hypergraphs, increasingly utilised to model complex and diverse relationships in modern networks, have gained significant attention for representing intricate higher-order interactions. Among various challenges, cohesive subgraph discovery is one of the fundamental problems and offers deep insights into these structures, yet the task of selecting appropriate parameters is an open question. To address this question, we aim to design an efficient indexing structure to retrieve cohesive subgraphs in an online manner. The main idea is to enable the discovery of corresponding structures within a reasonable time without the need for exhaustive graph traversals. Our method enables faster and more effective retrieval of cohesive structures, which supports decision-making in applications that require online analysis of large-scale hypergraphs. Through extensive experiments on real-world networks, we demonstrate the superiority of our proposed indexing technique.