Extending modularity by capturing the similarity attraction feature in the null model

TL;DR

This paper introduces a new null model for modularity that incorporates the similarity attraction feature, improving community detection in networks by better reflecting real-world connection preferences.

Contribution

The paper proposes a null model capturing similarity attraction, enabling a flexible framework for community detection across various networks.

Findings

Dist-Modularity effectively identifies communities at multiple scales

The new null model better reflects real-world network features

Framework adapts to networks with additional node information

Abstract

Modularity is a widely used measure for evaluating community structure in networks. The definition of modularity involves a comparison of within-community edges in the observed network and that number in an equivalent randomized network. This equivalent randomized network is called the null model, which serves as a reference. To make the comparison significant, the null model should characterize some features of the observed network. However, the null model in the original definition of modularity is unrealistically mixed, in the sense that any node can be linked to any other node without preference and only connectivity matters. Thus, it fails to be a good representation of real-world networks. A common feature of many real-world networks is "similarity attraction", i.e., edges tend to link to nodes that are similar to each other. We propose a null model that captures the similarity attraction feature. This null model enables us to create a framework for defining a family of Dist-Modularity adapted to various networks, including networks with additional information on nodes. We demonstrate that Dist-Modularity is useful in identifying communities at different scales.