Local community detection by local structure expansion and exploring the local communities for target nodes in complex networks

Hao Shang Ma, Shiou Chi Li, Zhi Jia Jian, You Hua Kuo, Jen Wei Huang

研究成果: Article同行評審

摘要

Most algorithms for local community detection select a seed node using a greedy algorithm and then expand it into a community using optimization functions. This paper presents a novel approach to community detection based on local expansion. The proposed Local Community Detection via LOcal Structure Expansion (CLOSE) algorithm features a novel connective function, which identifies a source node in the center of a highly-connected component of a graph. The CLOSE algorithm also selects a group of nodes rather than a single node as a seed for local community expansion, which facilitates the selection of a community suitable for the hub node. We also developed a system by which to identify the most suitable source nodes for given target nodes, referred to as Exploring Local Communities of Target Nodes (ELCTN). The performance of CLOSE and ELCTN was compared with that of state-of-the-art methods in experiments using synthetic networks generated using the Lancichinetti-Fortunato-Radicchi benchmark as well as real-world networks. Both algorithms outperformed previous methods in terms of accuracy and modularity.

原文English
頁(從 - 到)499-515
頁數17
期刊Journal of Information Science and Engineering
37
發行號3
DOIs
出版狀態Published - 2021 五月

All Science Journal Classification (ASJC) codes

  • 軟體
  • 人機介面
  • 硬體和架構
  • 圖書館與資訊科學
  • 計算機理論與數學

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