Graph Embedding Based on Neighbor Similarity for Community Detection

doi:10.15966/j.cnki.dnydx.2024.05.023

Computer & Telecommunication

2024, Vol. 1

Issue (5): 79- DOI: 10.15966/j.cnki.dnydx.2024.05.023

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Graph Embedding Based on Neighbor Similarity for Community Detection

College of Computer and Artificial Intelligence

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Abstract Community detection is a crucial research topic in the realm of complex networks. Understanding and identifying the community structure of a network is essential for uncovering its behavior and function. In this paper, we propose a novel graph em‐ bedding method based on neighbor similarity for community detection. By utilizing the acceptance of nodes and aggregating attri‐ bute information expressions of neighbors, we obtain the vector representation of each node in the network. The final community de‐ tection results are then obtained by directly applying K-means clustering. Our experimental results demonstrate that our proposed al‐ gorithm outperforms other methods, showing significant improvements in both modularity and standard normalization metrics.

Key words： community detection neighbor similarity graph embedding clustering

Published: 12 October 2024

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ZHANG An-qi　ZHANG Na. Graph Embedding Based on Neighbor Similarity for Community Detection. Computer & Telecommunication, 2024, 1(5): 79-.

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https://www.computertelecom.com.cn/EN/10.15966/j.cnki.dnydx.2024.05.023 OR https://www.computertelecom.com.cn/EN/Y2024/V1/I5/79

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