Community detection in temporal citation network via a tensor-based approach

Abstract

In the era of big data, network analysis has attracted widespread attention. Detecting and tracking community evolution in temporal networks can uncover important and interesting behaviors. In this paper, we analyze a temporal citation network constructed by publications collected from 44 statistical journals between 2001 and 2018. We propose an approach named Tensor-based Directed Spectral Clustering On Ratios of Eigenvectors (TD-SCORE) which can correct for degree heterogeneity to detect the community structure of the temporal citation network. We first explore the characteristics of the temporal network via in-degree distribution and visualization of different snapshots, and we find that both the community structure and the key nodes change over time. Then, we apply the TD-SCORE method to the core network of our temporal citation network. Seven communities are identified, including variable selection, Bayesian analysis, functional data analysis, and many others. Finally, we track the evolution of the above communities and reach some conclusions.