Community evolution prediction based on a self-adaptive timeframe in social networks

Abstract

The prediction of community evolution events in dynamic social networks is of great importance for network security alerts. Currently, most of the common prediction models use a fixed timeframe division strategy to divide the dynamic social network, such as the disjoint timeframe division strategy and overlapping timeframe division strategy. However, these frameworks cannot change once they are selected, and are not suitable for the prediction of network evolution with strong variability in real-world applications. In this paper, a new community evolution model is developed from the perspective of the universality of the timeframe, and a new optimized timeframe partitioning algorithm is proposed. Compared with the traditional fixed timeframe partitioning algorithm, this method adaptively adjusts the size and number of time windows according to the information fluctuations of the specific network at an acceptable extra computational cost. Based on the analysis of several real-world networks, we found that the proposed self-adaptive timeframe partitioning algorithm improved the quality of community tracking of the network and ensured the accuracy of prediction events.