Wang Shenglong, Yang Jing, Ding Xiaoyu, Zhao Meng
College of Computer Science and Technology, Harbin Engineering University, Harbin, China.
Chongqing University of Posts and Telecommunications, Chongqing, China.
PeerJ Comput Sci. 2023 May 15;9:e1386. doi: 10.7717/peerj-cs.1386. eCollection 2023.
The goal of local community detection algorithms is to explore the optimal community with a reference to a given node. Such algorithms typically include two primary processes: seed selection and community expansion. This study develops and tests a novel local community detection algorithm called that is based on the optimization of interaction relationships between nodes and the community. First, we introduce an improved seed selection method to solve the seed deviation problem. Second, this study uses a series of similarity indices to measure the interaction relationship between nodes and community. Third, this study uses a series of algorithms based on different similarity indices, and designs experiments to reveal the role of the similarity index in algorithms based on relationship optimization. The proposed algorithm was compared with five existing local community algorithms in both real-world networks and artificial networks. Experimental results show that the optimization of interaction relationship algorithms based on node similarity can detect communities accurately and efficiently. In addition, a good similarity index can highlight the advantages of the proposed algorithm based on interaction optimization.
局部社区检测算法的目标是参照给定节点探索最优社区。此类算法通常包括两个主要过程：种子选择和社区扩展。本研究开发并测试了一种名为 的新型局部社区检测算法，该算法基于节点与社区之间交互关系的优化。首先，我们引入一种改进的种子选择方法来解决种子偏差问题。其次，本研究使用一系列相似性指标来衡量节点与社区之间的交互关系。第三，本研究使用基于不同相似性指标的一系列算法，并设计实验来揭示相似性指标在基于关系优化的算法中的作用。在真实网络和人工网络中，将所提出的算法与五种现有的局部社区算法进行了比较。实验结果表明，基于节点相似性的交互关系算法优化能够准确、高效地检测社区。此外，良好的相似性指标可以突出基于交互优化的所提出算法的优势。
