关键词: 信息网络, 社团演化, 层次聚类

Abstract: Community structure is an important feature that exists extensively in real-world complex networks. Traditional 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 proposes 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 this paper greatly improves the algorithm efficiency and stability.

Key words: information networks, community evolution, hierarchical clustering