Self-falsifiable Hierarchical Detection of Overlapping Communities On Social Networks

TL;DR

This paper introduces a parameter-free, hierarchical, overlapping community detection algorithm for social networks that can self-assess its suitability for a given network, demonstrating robustness and revealing intrinsic community features.

Contribution

The paper presents a novel self-falsifiable, hierarchical community detection method based on label propagation that automatically evaluates its effectiveness for specific networks.

Findings

Algorithm is self-consistent and reliable across diverse networks

More robust than existing methods on sparse and large-scale networks

Uncovers features of networks' intrinsic community structures

Abstract

No community detection algorithm can be optimal for all possible networks, thus it is important to identify whether the algorithm is suitable for a given network. We propose a multi-step algorithmic solution scheme for overlapping community detection based on an advanced label propagation process, which imitates the community formation process on social networks. Our algorithm is parameter-free and is able to reveal the hierarchical order of communities in the graph. The unique property of our solution scheme is self-falsifiability; an automatic quality check of the results is conducted after the detection, and the fitness of the algorithm for the specific network is reported. Extensive experiments show that our algorithm is self-consistent, reliable on networks of a wide range of size and different sorts, and is more robust than existing algorithms on both sparse and large-scale social networks. Results further suggest that our solution scheme may uncover features of networks' intrinsic community structures.