基于事件的社会网络演化分析框架  

Framework for Tracking the Event-Based Evolution in Social Networks

作　　者：吴斌[1] 王柏[1] 杨胜琦[1] 

机构地区：[1]北京邮电大学北京市智能通信软件与多媒体重点实验室,北京100876

出　　处：《软件学报》2011年第7期1488-1502,共15页Journal of Software

基　　金：国家自然科学基金(90924029;60905025);国家科技支撑计划(2006BAH03B05)

摘　　要：提出了一个全新的复杂网络分析框架来跟踪动态网络的演化规律,发现其在演化过程中的时间特性.不同于传统静态时间片的分析方法,整个框架首先利用有效而快速的方法发现网络的timeline,然后利用图近似算法刻画timeline中的平稳演化段落,这样可以有效地降低个体行为的不确定性所带来的网络演化噪声.此外,综合考虑到网络中个体的多维属性,还提出一种高效的社团发现算法,用以发现动态网络中的社团结构.为了对社团进行演化分析,提出了社团演化的评价方法,以发现社团演化过程的动态特征.最后,为了示例该框架的有效性和实用性,整个框架被应用于多个实际的网络数据集,并且揭示了这些网络在演化过程中的时间特性及社团演化模式.This paper presents a fundamentally different framework for uncovering the intricate properties of evolutionary networks. Contrary to static snapshots methods, this paper first traces the timelines of the networks. Then, based on extracted smooth segments from the timelines, a graph approximation algorithm is applied to capture the frequent characteristics of the network and reduce the noise of interactions. Moreover, by employing the relationship among multi-attributes, an innovative community detection algorithm is proposed for a detailed analysis on the approximate graphs. To track these dynamic communities, this paper also introduces a community correlation and evaluation method. Finally, by applying this novel framework to several real-world networks, this paper demonstrates the critical relationship between event and social evolution, and reveals meaningful properties in actual dynamic behaviors.

关 键 词：社会网络 演化 动态模式 社团发现 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]