| 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.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. |
| 关键词: 社会网络 演化 动态模式 社团发现 |
| DOI：10.3724/SP.J.1001.2011.03841 |
| 分类号: |
| 基金项目:国家自然科学基金(90924029, 60905025); 国家科技支撑计划(2006BAH03B05) |
|
| Framework for Tracking the Event-Based Evolution in Social Networks |
|
WU Bin, WANG Bai, YANG Sheng-Qi
|
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Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and
Telecommunications, Beijing 100876, China
|
| Abstract: |
| 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. |
| Key words: social network evolution dynamic pattern community detection |
