Community Detection based on Node Relationship Classification

Shunjie Yuan, Hefeng Zeng, Chao Wang

2022

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.

Paper Citation

in Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 596-601. DOI: 10.5220/0010850600003122

in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2022},
pages={596-601},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010850600003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Community Detection based on Node Relationship Classification
SN - 978-989-758-549-4
AU - Yuan S.
AU - Zeng H.
AU - Wang C.
PY - 2022
SP - 596
EP - 601
DO - 10.5220/0010850600003122