Block-corrected Modularity for Community Detection

TL;DR

This paper introduces a block-corrected modularity approach for community detection that discounts known block structures to uncover hidden communities, demonstrating effectiveness on synthetic and real-world networks.

Contribution

It proposes a novel modularity measure that accounts for known block structures, improving community detection accuracy in complex networks.

Findings

Successfully detects hidden communities in synthetic networks

Outperforms existing methods on benchmark models

Effective correction for temporal citation patterns in real-world data

Abstract

Unknown node attributes in complex networks may introduce community structures that are important to distinguish from those driven by known attributes. We propose a block-corrected modularity that discounts given block structures present in the network to reveal communities masked by them. We show analytically how the proposed modularity finds the community structure driven by an unknown attribute in a simple network model. Further, we observe that the block-corrected modularity finds the underlying community structure on a number of simple synthetic network models while methods using different null models fail. We develop an efficient spectral method as well as two Louvain-inspired fine-tuning algorithms to maximize the proposed modularity and demonstrate their performance on several synthetic network models. Finally, we assess our methodology on various real-world citation networks built using the OpenAlex data by correcting for the temporal citation patterns.