Community detection on directed networks with missing edges

TL;DR

This paper extends a flow stability framework to detect communities in directed, weighted networks with missing edges, utilizing uncertainty estimates to improve robustness in incomplete data scenarios.

Contribution

It introduces a novel method that incorporates uncertainty in out-degree measurements to enhance community detection in incomplete directed networks.

Findings

Improved community detection robustness with missing data

Effective on synthetic and real-world Telegram network

Outperforms existing methods in reliability

Abstract

Identifying significant community structures in networks with incomplete data is a challenging task, as the reliability of solutions diminishes with increasing levels of missing information. However, in many empirical contexts, some information about the uncertainty in the network measurements can be estimated. In this work, we extend the recently developed Flow Stability framework, originally designed for detecting communities in time-varying networks, to address the problem of community detection in weighted, directed networks with missing links. Our approach leverages known uncertainty levels in nodes' out-degrees to enhance the robustness of community detection. Through comparisons on synthetic networks and a real-world network of messaging channels on the Telegram platform, we demonstrate that our method delivers more reliable community structures, even when a significant portion of data is missing.