Measuring and Modeling Bipartite Graphs with Community Structure

TL;DR

This paper introduces two generative models for bipartite graphs that accurately replicate real-world degree distributions and community structures, measured by the metamorphosis coefficient, enhancing understanding of bipartite network connectivity.

Contribution

The paper presents two novel bipartite graph models, bipartite Chung-Lu and bipartite BTER, that quantitatively match real-world network characteristics including degree distributions and community structure indicators.

Findings

Bipartite Chung-Lu reproduces degree distributions effectively.

Bipartite BTER captures both degree distributions and metamorphosis coefficients.

Models are validated on multiple real-world datasets.

Abstract

Network science is a powerful tool for analyzing complex systems in fields ranging from sociology to engineering to biology. This paper is focused on generative models of large-scale bipartite graphs, also known as two-way graphs or two-mode networks. We propose two generative models that can be easily tuned to reproduce the characteristics of real-world networks, not just qualitatively, but quantitatively. The characteristics we consider are the degree distributions and the metamorphosis coefficient. The metamorphosis coefficient, a bipartite analogue of the clustering coefficient, is the proportion of length-three paths that participate in length-four cycles. Having a high metamorphosis coefficient is a necessary condition for close-knit community structure. We define edge, node, and degreewise metamorphosis coefficients, enabling a more detailed understanding of the bipartite connectivity that is not explained by degree distribution alone. Our first model, bipartite Chung-Lu (CL), is able to reproduce real-world degree distributions, and our second model, bipartite block two-level Erd\"os-R\'enyi (BTER), reproduces both the degree distributions as well as the degreewise metamorphosis coefficients. We demonstrate the effectiveness of these models on several real-world data sets.