Bridge Bounding: A Local Approach for Efficient Community Discovery in Complex Networks

TL;DR

Bridge Bounding is a local community detection method that efficiently identifies community boundaries in large networks by exploring local topology around seed nodes, enabling scalable analysis of web-based social networks.

Contribution

The paper introduces Bridge Bounding, a novel local approach for community detection that improves efficiency and scalability over global methods in analyzing large complex networks.

Findings

Effective in detecting communities in large web networks

Outperforms some state-of-the-art global methods

Applicable to real-world social network data

Abstract

The increasing importance of Web 2.0 applications during the last years has created significant interest in tools for analyzing and describing collective user activities and emerging phenomena within the Web. Network structures have been widely employed in this context for modeling users, web resources and relations between them. However, the amount of data produced by modern web systems results in networks that are of unprecedented size and complexity, and are thus hard to interpret. To this end, community detection methods attempt to uncover natural groupings of web objects by analyzing the topology of their containing network. There are numerous techniques adopting a global perspective to the community detection problem, i.e. they operate on the complete network structure, thus being computationally expensive and hard to apply in a streaming manner. In order to add a local perspective to the study of the problem, we present Bridge Bounding, a local methodology for community detection, which explores the local network topology around a seed node in order to identify edges that act as boundaries to the local community. The proposed method can be integrated in an efficient global community detection scheme that compares favorably to the state of the art. As a case study, we apply the method to explore the topic structure of the LYCOS iQ collaborative question/answering application by detecting communities in the networks created from the collective tagging activity of users.