拓扑结构与节点属性综合分析的社区发现算法  

A Community Detecting Algorithm Based on Comprehensive Analysis of Network Topology and Node Attributes

作　　者：张振宇 朱培栋[1] 王可 胡慧俐[1] ZHANG Zhen-yu;ZHU Pei-dong;WANG Ke;HU Hui-li(School of Computer,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科学技术大学计算机学院,湖南长沙410073

出　　处：《计算机技术与发展》2018年第4期1-5,共5页Computer Technology and Development

基　　金：国家自然科学基金(61572514;61379117)

摘　　要：社交网络的社区发现对于理解网络功能、识别网络连接层次性及预测社交网络用户的复杂群体行为有着极其重要的基础性作用。鉴于现有社区发现算法通常只基于拓扑结构或节点属性单种因素提出,提出一种综合两方面因素的社区发现算法。该算法首先基于Spearman相关系数对初始数据进行去相关性处理,避免后续分析的相关性误差;然后引入后验概率理论进行稳定性赋权,综合拓扑与属性两影响因素;最后根据模糊传递闭包原理,从关系变换的角度进行社区发现。与经典社区发现算法相比,该算法不仅提高了社区发现的准确性,且在一定程度上解决了社区结构中网络动态性影响及社区层次性问题。Community detecting of social networks plays a significant role in understanding network functions,identifying network connection levels and predicting complex behaviors of social network users.Given that existing community detecting algorithms are mainly based on single factor involving the topology or attribute,we present a community detecting algorithm integrated two factors.To start with,the initial data is decorrelated based on the Spearman correlation coefficient to avoid the correlation error of the following analysis.In addition,the posterior probability theory is applied to weight stabilization by integrating topology and attribute.At last,according to the principle of fuzzy transfer closure,community can be detected from the perspective of transforming relation.Compared with classical community detecting algorithms,the proposed algorithm not only improves the accuracy,but also solves problems of network dynamics and community level in community structure to a certain extent.

关 键 词：社区发现 Spearman相关性 后验概率 模糊传递闭包 社区层次 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]