Distribution of maximal clique size of the vertices for theoretical small-world networks and real-world networks

TL;DR

This paper investigates the distribution of maximal clique sizes of vertices in complex networks, revealing Poisson-like distributions in small-world and real-world networks and analyzing their correlation with clustering and assortativity.

Contribution

It introduces a modified branch-and-bound algorithm to determine vertex maximal clique sizes and analyzes their distribution and correlations across different network types and evolution stages.

Findings

Maximal clique size distribution follows a Poisson-like pattern in studied networks.

Maximal clique size remains invariant during small-world network evolution.

Significant correlation between maximal clique size, clustering coefficient, and node degree.

Abstract

Our primary objective in this paper is to study the distribution of the maximal clique size of the vertices in complex networks. We define the maximal clique size for a vertex as the maximum size of the clique that the vertex is part of and such a clique need not be the maximum size clique for the entire network. We determine the maximal clique size of the vertices using a modified version of a branch-and-bound based exact algorithm that has been originally proposed to determine the maximum size clique for an entire network graph. We then run this algorithm on two categories of complex networks: One category of networks capture the evolution of small-world networks from regular network (according to the wellknown Watts-Strogatz model) and their subsequent evolution to random networks; we show that the distribution of the maximal clique size of the vertices follows a Poisson-style distribution at different stages of the evolution of the small-world network to a random network; on the other hand, the maximal clique size of the vertices is observed to be in-variant and to be very close to that of the maximum clique size for the entire network graph as the regular network is transformed to a small-world network. The second category of complex networks studied are real-world networks (ranging from random networks to scale-free networks) and we observe the maximal clique size of the vertices in five of the six real-world networks to follow a Poisson-style distribution. In addition to the above case studies, we also analyze the correlation between the maximal clique size and clustering coefficient as well as analyze the assortativity index of the vertices with respect to maximal clique size and node degree.