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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.22850 |
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| _version_ | 1866914116228087808 |
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| author | Georgiadis, Nikolaos Tiakas, Eleftherios Papadopoulos, Apostolos N. |
| author_facet | Georgiadis, Nikolaos Tiakas, Eleftherios Papadopoulos, Apostolos N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22850 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Community Search in Attributed Networks using Dominance Relationships and Random Walks Georgiadis, Nikolaos Tiakas, Eleftherios Papadopoulos, Apostolos N. Social and Information Networks 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. |
| title | Community Search in Attributed Networks using Dominance Relationships and Random Walks |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2510.22850 |