Affiliation-based Local Community Detection across Multiple Networks

TL;DR

The paper introduces LAMA, a novel local algorithm for community detection across multiple networks, effectively capturing community structures in multi-domain and multi-view networks by optimizing node affiliations.

Contribution

LAMA is a new algorithm designed specifically for multi-domain networks, addressing limitations of existing methods for multi-view networks.

Findings

LAMA outperforms existing algorithms on synthetic datasets.

LAMA achieves superior results on five real-world datasets.

The method effectively detects communities across diverse network types.

Abstract

Real-world networks are often constructed from different sources or domains, including various types of entities and diverse relationships between networks, thus forming multi-domain networks. A single network typically fails to capture the complete graph structure and the diverse relationships among multiple networks. Consequently, leveraging multiple networks is crucial for a comprehensive detection of community structures. Most existing local community detection methods discover community structures by integrating information from different views on multi-view networks. However, methods designed for multi-view networks are not suitable for multi-domain networks. Therefore, to mine communities from multiple networks, we propose a Local Algorithm for Multiple networks with node Affiliation, called LAMA, which is suitable for both multi-view and multi-domain networks. The core idea of LAMA is to optimize node affiliations by maximizing the quality of communities within each network while ensuring consistency in community structures across multiple networks. The algorithm iteratively optimizes node affiliations and expands the community outward based on affiliations to detect the community containing the seed node. Experimental results show that LAMA outperforms comparison algorithms on two synthetic datasets and five real datasets.