GI-OHMS: Graphical Inference to Detect Overlapping Communities

TL;DR

This paper introduces GI-OHMS, a novel graphical inference method that transforms complex networks into a simplified form to accurately and efficiently detect overlapping communities, outperforming existing algorithms in accuracy and speed.

Contribution

The paper presents a new approach that converts networks into an observed-hidden merged seeded form and applies Bayesian Markov Random Fields for improved overlapping community detection.

Findings

Outperforms baseline algorithms like OSLOM, DEMON, and LEMON in accuracy.

Achieves significant speed-up with multi-threaded implementation.

Effectively captures community properties in complex, evolving networks.

Abstract

Discovery of communities in complex networks is a topic of considerable recent interest within the complex systems community. Due to the dynamic and rapidly evolving nature of large-scale networks, like online social networks, the notion of stronger local and global interactions among the nodes in communities has become harder to capture. In this paper, we present a novel graphical inference method - GI-OHMS (Graphical Inference in Observed-Hidden variable Merged Seeded network) to solve the problem of overlapping community detection. The novelty of our approach is in transforming the complex and dense network of interest into an observed-hidden merged seeded(OHMS) network, which preserves the important community properties of the network. We further utilize a graphical inference method (Bayesian Markov Random Field) to extract communities. The superiority of our approach lies in two main observations: 1) The extracted OHMS network excludes many weaker connections, thus leading to a higher accuracy of inference 2) The graphical inference step operates on a smaller network, thus having much lower execution time. We demonstrate that our method outperforms the accuracy of other baseline algorithms like OSLOM, DEMON, and LEMON. To further improve execution time, we have a multi-threaded implementation and demonstrate significant speed-up compared to state-of-the-art algorithms.