Influence of reciprocal arcs on the degree distribution and degree correlations

TL;DR

This paper investigates how reciprocal arcs affect degree distribution and correlations in directed networks, revealing fundamental differences between static and evolving networks and proposing a model that bridges undirected and directed BA networks.

Contribution

It introduces a method to infer reciprocal arcs due to feedback and presents a growing network model interpolating between undirected and directed BA models.

Findings

Reciprocal arcs significantly influence degree correlations.

A statistical method estimates reciprocal arcs explained by feedback.

A new network growth model bridges undirected and directed BA models.

Abstract

Reciprocal arcs represent the lowest order cycle possible to find in directed graphs without self-loops. Representing also a measure of feed-back between vertices, it is interesting to understand how reciprocal arcs influence other properties of complex networks. In this paper we focus on influence of reciprocal arcs on vertex degree distribution and degree correlations. We show that there is a fundamental difference between properties observed on the static network compared to the properties of networks which are obtained by simple evolution mechanism driven by reciprocity. We also present a way to statistically infer the portion of reciprocal arcs which can be explained as a consequence of feed-back process on the static network. In the rest of the paper the influence of reciprocal arcs on a model of growing network is also presented. It is shown that our model of growing network nicely interpolates between BA model for undirected and the BA model for directed networks.