Basic Notions for the Analysis of Large Affiliation Networks / Bipartite Graphs

TL;DR

This paper extends fundamental network analysis concepts to bipartite graphs, introducing simple statistics to systematically analyze large affiliation networks and compare real-world data with random models.

Contribution

It proposes a systematic extension of classical network analysis tools specifically tailored for bipartite networks, with new statistical measures.

Findings

Statistics differentiate real bipartite networks from random models.

Real-world bipartite networks exhibit distinct structural properties.

The proposed methods facilitate better understanding of large affiliation networks.

Abstract

Many real-world complex networks actually have a bipartite nature: their nodes may be separated into two classes, the links being between nodes of different classes only. Despite this, and despite the fact that many ad-hoc tools have been designed for the study of special cases, very few exist to analyse (describe, extract relevant information) such networks in a systematic way. We propose here an extension of the most basic notions used nowadays to analyse classical complex networks to the bipartite case. To achieve this, we introduce a set of simple statistics, which we discuss by comparing their values on a representative set of real-world networks and on their random versions.