Preferential attachment hypergraph with vertex deactivation

TL;DR

This paper introduces a new hypergraph model with vertex deactivation, capturing real-world network behaviors and showing that its degree distribution follows a power-law with an exponential cutoff, aligning with empirical data.

Contribution

The paper proposes a novel preferential attachment hypergraph model incorporating vertex deactivation, a feature rarely modeled but common in real networks.

Findings

Degree distribution follows a power-law with exponential cutoff.

Model aligns with empirical data from a collaboration network.

Vertex deactivation is crucial for realistic network modeling.

Abstract

In the field of complex networks, hypergraph models have so far received significantly less attention than graphs. However, many real-life networks feature multiary relations (co-authorship, protein reactions) may therefore be modeled way better by hypergraphs. Also, a recent study by Broido and Clauset suggests that a power-law degree distribution is not as ubiquitous in the natural systems as it was thought so far. They experimentally confirm that a majority of networks (56% of around 1000 networks that undergone the test) favor a power-law with an exponential cutoff over other distributions. We address the two above observations by introducing a preferential attachment hypergraph model which allows for vertex deactivations. The phenomenon of vertex deactivations is rare in existing theoretical models and omnipresent in real-life scenarios (social network accounts which are not maintained forever, collaboration networks in which people retire, technological networks in which devices break down). We prove that the degree distribution of the proposed model follows a power-law with an exponential cutoff. We also check experimentally that a Scopus collaboration network has the same characteristic. We believe that our model will predict well the behavior of systems from a variety of domains.