Higher-order distributions and nongrowing complex networks without multiple connections

TL;DR

This paper investigates stochastic processes for generating non-growing simple networks, introducing a new wedge distribution concept to understand constraints that prevent multiple edges and self-loops, highlighting the complexity of modeling such networks.

Contribution

It introduces the concept of wedge distribution to analyze constraints in simple graph generation and explores the limitations of existing stochastic processes for these networks.

Findings

Constraints prevent a closed master equation in general cases

Wedge distribution relates to degree-degree correlation

A new process without edge selection rules offers insights

Abstract

We study stochastic processes that generate non-growing complex networks without self-loops and multiple edges (simple graphs). The work concentrates on understanding and formulation of constraints which keep the rewiring stochastic processes within the class of simple graphs. To formulate these constraints a new concept of wedge distribution (paths of length 2) is introduced and its relation to degree-degree correlation is studied. The analysis shows that the constraints, together with edge selection rules, do not even allow to formulate a closed master equation in the general case. We also introduce a particular stochastic process which does not contain edge selection rules, but which, we believe, can provide some insight into the complexities of simple graphs.