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Authors: Shunjie Yuan ; Hefeng Zeng and Chao Wang

Affiliation: School of Cyber Engineering, Xidian University, Xi’an 710126, China

Keyword(s): Complex Network, Community Detection, Machine Learning.

Abstract: Community detection is a salient task in network analysis to understand the intrinsic structure of networks. In this paper, we propose a novel community detection algorithm based on node relationship classification. The node relationship between two neighboring nodes is defined as whether they affiliate to the same community. A trained binary classifier is deployed to classify the node relationship, which considers both the local influence from the two nodes themselves and the global influence from the whole network. According to the classified node relationship, community structure can be detected naturally. The experimental results on both real-world and synthetic networks demonstrate that our algorithm has a better performance compared to other representative algorithms.

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Paper citation in several formats:
Yuan, S.; Zeng, H. and Wang, C. (2022). Community Detection based on Node Relationship Classification. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-549-4; ISSN 2184-4313, SciTePress, pages 596-601. DOI: 10.5220/0010850600003122

@conference{icpram22,
author={Shunjie Yuan. and Hefeng Zeng. and Chao Wang.},
title={Community Detection based on Node Relationship Classification},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2022},
pages={596-601},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010850600003122},
isbn={978-989-758-549-4},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Community Detection based on Node Relationship Classification
SN - 978-989-758-549-4
IS - 2184-4313
AU - Yuan, S.
AU - Zeng, H.
AU - Wang, C.
PY - 2022
SP - 596
EP - 601
DO - 10.5220/0010850600003122
PB - SciTePress

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