Hub-Based Community Finding
Luciano da Fontoura Costa

TL;DR
This paper introduces a hub-based method for community detection in complex networks, utilizing wavefronts from high-degree nodes to identify communities and their boundaries across various network types.
Contribution
It presents a simple, effective approach for community finding that works on different network types and includes boundary detection, demonstrated on multiple datasets.
Findings
Effective in identifying communities in various networks
Applicable to weighted and unweighted networks
Capable of boundary detection between communities
Abstract
This article presents a hub-based approach to community finding in complex networks. After identifying the network nodes with highest degree (the so-called hubs), the network is flooded with wavefronts of labels emanating from the hubs, accounting for the identification of the involved communities. The simplicity and potential of this method, which is presented for direct/undirected and weighted/unweighted networks, is illustrated with respect to the Zachary karate club data, image segmentation, and concept association. Attention is also given to the identification of the boundaries between communities.
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Taxonomy
TopicsComplex Network Analysis Techniques · Data Visualization and Analytics · Advanced Clustering Algorithms Research
