基于局部优势指数的贪婪扩展网络社区检测
Detecting network communities via greedy expanding based on local superiority index
摘要：
社区发现是网络科学中一个重要而又具有挑战性的课题。近年来，社区发现的局部方法受到了广泛的关注。贪婪扩展算法是一种流行而有效的局部算法，它通常从选定的中心节点开始，通过优化某个质量函数来扩展这些节点以获得临时社区。在本文中，我们提出了一种新的指数，称为本地优势指数（LSI），以确定中心节点。在扩展过程中，我们应用适应度函数来估计临时社区的质量，并确保所有临时社区必须是弱社区。基于归一化互信息的评价结果表明：（1）在考虑最多的网络上，LSI优于全局最大度指标和局部最大度指标;（2）在考虑最多的网络上，基于LSI的贪婪算法优于经典的快速算法。
英文摘要：
Community detection is a significant and challenging task in network science. Nowadays, plenty of attention has been paid on local methods for community detection. Greedy expanding is a popular and efficient class of local algorithms, which typically starts from some selected central nodes and expands those nodes to obtain provisional communities by optimizing a certain quality function. In this paper, we propose a novel index, called local superiority index (LSI), to identify central nodes. In the process of expansion, we apply the fitness function to estimate the quality of provisional communities and ensure that all provisional communities must be weak communities. Evaluation based on the normalized mutual information suggests: (1) LSI is superior to the global maximal degree index and the local maximal degree index on most considered networks; (2) The greedy algorithm based on LSI is better than the classical fast algorithm on most considered networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
