Community detection for directed networks revisited using bimodularity

TL;DR

This paper introduces a novel bimodularity framework for detecting directed communities in networks, utilizing convex relaxation and singular value decomposition, demonstrated on synthetic data and neuronal wiring diagrams.

Contribution

It proposes a new bimodularity concept for directed community detection, extending modularity optimization to directed networks with a novel convex relaxation approach.

Findings

Successfully applied to synthetic models and C. elegans neuronal data.

Revealed meaningful feedforward loops in neuronal wiring.

Provides a new framework for understanding directed community structures.

Abstract

Community structure is a key feature omnipresent in real-world network data. Plethora of methods have been proposed to reveal subsets of densely interconnected nodes using criteria such as the modularity index. These approaches have been successful for undirected graphs, but directed edge information has not yet been dealt with in a satisfactory way. Here, we revisit the concept of directed communities as a mapping between sending and receiving communities. This translates into a new definition that we term bimodularity. Using convex relaxation, bimodularity can be optimized with the singular value decomposition of the directed modularity matrix. Subsequently, we propose an edge-based clustering approach to reveal the directed communities including their mappings. The feasibility of the new framework is illustrated on a synthetic model and further applied to the neuronal wiring diagram of the \textit{C. elegans}, for which it yields meaningful feedforward loops of the head and body motion systems. This framework sets the ground for the understanding and detection of community structures in directed networks.