基于结构分析的信息网络社团趋势预测

Information Networks Community Trend Prediction Based on Structure Analysis

社团结构在现实世界各种信息网络中广泛存在。传统信息网络中社团演化的研究均基于单一层次的观察与分析,存在算法不稳定,无法处理社团结构剧烈变化等问题。为解决该问题,提出了基于结构分析的信息网络社团趋势预测方法。该方法基于层次聚类来发现社团层次结构,对相邻网络快照的社团进行跨层次匹配,以解决社团发现算法带来的随机性问题,且使基于结构的社团演化研究成为可能。在两个真实数据集上进行了多层次社团演化挖掘实验,实验结果表明,与最优划分方法相比,新方法在效率和稳定性方面有较大优势。

Community structure is an important feature that exists extensively in real-world complex networks. Tradi- tional community evolution studies are limited to the analysis on single-level communities, and have some defects, such as the evolutionary regularities revealing and algorithms stability, etc. To handle the problems, this paper pro- poses an information networks community trend prediction method based on structure analysis. The method obtains community hierarchies by hierarchical clustering, matches communities with different structures in adjacent network snapshots, therefore relatively overcomes the difficulty of overlooking the influence of sudden outside events, and provides possibility for the structure based community evolution analysis. The method is applied in two real-world datasets, and the experimental results show that the work in stability. this paper greatly improves the algorithm efficiency and stability.