Hypergraph Laplacians in Diffusion Framework

TL;DR

This paper introduces two novel hypergraph Laplacians based on a diffusion framework, enabling modeling of higher-order interactions and diffusion processes on hypergraphs for various network analysis tasks.

Contribution

The paper proposes new hypergraph Laplacians that incorporate higher-order relations, advancing the modeling of diffusion processes on hypergraphs.

Findings

Laplacians account for higher-order interactions

Applicable to social contagion and influence studies

Enhances hypergraph classification methods

Abstract

Networks are important structures used to model complex systems where interactions take place. In a basic network model, entities are represented as nodes, and interaction and relations among them are represented as edges. However, in a complex system, we cannot describe all relations as pairwise interactions, rather should describe as higher-order interactions. Hypergraphs are successfully used to model higher-order interactions in complex systems. In this paper, we present two new hypergraph Laplacians based on diffusion framework. Our Laplacians take the relations between higher-order interactions into consideration, hence can be used to model diffusion on hypergraphs not only between vertices but also higher-order structures. These Laplacians can be employed in different network mining problems on hypergraphs, such as social contagion models on hypergraphs, influence study on hypergraphs, and hypergraph classification, to list a few.