Community Search in Attributed Networks using Dominance Relationships and Random Walks

TL;DR

This paper presents a new algorithm for community search in attributed networks that combines structural and attribute-based methods using domination scores and $k$-core extraction, effectively identifying cohesive communities.

Contribution

The paper introduces a novel algorithm integrating hop-based and random-walk methods with domination scores and $k$-core extraction for attributed community detection.

Findings

Efficiently identifies cohesive communities in large real-world datasets.

Balances structural connectivity and attribute similarity effectively.

Suitable for social network analysis and recommendation systems.

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.