论文标题
根据当地优势指数来检测通过贪婪扩展的网络社区
Detecting network communities via greedy expanding based on local superiority index
论文作者
论文摘要
社区检测是网络科学中的一项重要且具有挑战性的任务。如今，人们对当地方法进行了广泛的关注。贪婪的扩展是一种流行而有效的本地算法类别，通常从某些选定的中央节点开始，并通过优化一定的质量功能来扩展这些节点以获得临时社区。在本文中，我们提出了一个新的指数，称为局部优势指数（LSI），以识别中央节点。在扩展过程中，我们将健身功能应用于估计临时社区的质量，并确保所有临时社区必须是弱社区。基于归一化信息的评估表明：（1）LSI优于大多数被考虑的网络上的全球最大程度指数和局部最大程度指数； （2）基于LSI的贪婪算法比大多数被考虑的网络上的经典快速算法更好。
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.
