Internal links and pairs as a new tool for the analysis of bipartite complex networks

TL;DR

This paper introduces internal links and pairs as novel tools for analyzing bipartite complex networks, providing insights into information loss from projections and enabling more effective modeling and storage.

Contribution

It presents new concepts of internal links and pairs to analyze bipartite networks, addressing limitations of traditional projection methods and improving network modeling and data storage.

Findings

Internal links and pairs reveal information lost in bipartite projections.

The concepts help discriminate behaviors in real-world networks.

They enable more compact network representations.

Abstract

Many real-world complex networks are best modeled as bipartite (or 2-mode) graphs, where nodes are divided into two sets with links connecting one side to the other. However, there is currently a lack of methods to analyze properly such graphs as most existing measures and methods are suited to classical graphs. A usual but limited approach consists in deriving 1-mode graphs (called projections) from the underlying bipartite structure, though it causes important loss of information and data storage issues. We introduce here internal links and pairs as a new notion useful for such analysis: it gives insights on the information lost by projecting the bipartite graph. We illustrate the relevance of theses concepts on several real-world instances illustrating how it enables to discriminate behaviors among various cases when we compare them to a benchmark of random networks. Then, we show that we can draw benefit from this concept for both modeling complex networks and storing them in a compact format.