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arXiv · 2510.22850

Community Search in Attributed Networks using Dominance Relationships and Random Walks

Nikolaos Georgiadis·Eleftherios Tiakas·Apostolos N. Papadopoulos
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Abstract

Community search in attributed networks poses a dual challenge: balancing structural connectivity -- the network's topological properties -- and attribute similarity -- the shared characteristics of nodes. This paper introduces a novel algorithm that integrates hop-based and random-walk-based methods to identify high-quality communities, effectively addressing this balance. Our approach employs the concept of the domination score to quantify the influence of nodes based on their attributes, followed by $k$-core extraction to ensure strong structural cohesion within the communities. By considering both the network structure and node attributes, the algorithm identifies communities that are not only well-connected, but also share meaningful attribute similarities. We evaluated the algorithm on large real-world datasets, demonstrating its ability to efficiently identify cohesive communities, making it suitable for applications such as social network analysis and recommendation systems.

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Nikolaos Georgiadis, Eleftherios Tiakas, Apostolos N. Papadopoulos. 2025-10-26. Community Search in Attributed Networks using Dominance Relationships and Random Walks. https://arxiv.org/abs/2510.22850

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