Graphon Mixtures

TL;DR

This paper introduces a graphon mixture model that captures both hub and dense community structures in social networks, enabling better analysis and generation of complex graph sequences.

Contribution

The paper proposes a novel graphon mixture model and a new max-degree condition for identifying hubs in sparse graphs, with theoretical estimation methods.

Findings

Successfully estimates hub degrees in synthetic and real networks

Effectively models both sparse and dense structures in social graphs

Demonstrates improved graph generation and analysis capabilities

Abstract

Social networks have a small number of large hubs, and a large number of small dense communities. We propose a generative model that captures both hub and dense structures. Based on recent results about graphons on line graphs, our model is a graphon mixture, enabling us to generate sequences of graphs where each graph is a combination of sparse and dense graphs. We propose a new condition on sparse graphs (the max-degree), which enables us to identify hubs. We show theoretically that we can estimate the normalized degree of the hubs, as well as estimate the graphon corresponding to sparse components of graph mixtures. We illustrate our approach on synthetic data, citation graphs, and social networks, showing the benefits of explicitly modeling sparse graphs.