Markov random walk under constraint for discovering overlapping communities in complex networks

TL;DR

This paper introduces UEOC, a Markov dynamics-based algorithm that effectively detects overlapping communities in complex networks using a constraint strategy and conductance-based cutoff, requiring minimal parameter tuning.

Contribution

The paper presents a novel Markov random walk algorithm with a constraint strategy for overlapping community detection, which is simple, parameter-efficient, and does not need prior knowledge.

Findings

UEOC outperforms competing algorithms on synthetic and real-world networks.

UEOC is highly effective and efficient in discovering overlapping communities.

The algorithm requires only one easily set parameter.

Abstract

Detection of overlapping communities in complex networks has motivated recent research in the relevant fields. Aiming this problem, we propose a Markov dynamics based algorithm, called UEOC, which means, 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge on the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and was compared with a set of competing algorithms. Experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities.