Modeling the Local and Global Evolution Pattern of Community Structures for Dynamic Networks Analysis

• Published in: IEEE access Vol. 7; pp. 71350 - 71360
• Main Authors: Yu, Wei, Wang, Wenjun, Jiao, Pengfei, Wu, Huaming, Sun, Yueheng, Tang, Minghu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Complex systems; Evolution; Evolution (biology); evolutionary pattern extraction; Feature extraction; Heuristic algorithms; Modelling; Network analysis; Orthogonal non-negative matrix factorization (ONMF); Pattern analysis; Predictive models; structure prediction; Task analysis; temporal community detection
• DOI: 10.1109/ACCESS.2019.2920237
• ISSN: 2169-3536

Exploring and understanding the temporal structure of dynamic networks attract extensive attention over the past few years. Most of these current research focuses on temporal community detection, evolution analysis or link prediction from a mission-oriented perspective. In fact, these three tasks should be not isolated but mutually reinforcing. Transforming these three tasks into a unified framework, it is crucial to extract the evolution pattern, which helps to understand the time-varying characteristics of temporal structure in essence. In addition, to the best of our knowledge, there is no work focusing on modeling and uncovering the local and global evolution pattern hidden in temporal community structure, simultaneously. In this paper, we propose a novel framework based on Orthogonal Nonnegative Matrix Factorization to Explore the Evolution Pattern (ONMF-EEP) for analyzing and predicting the time-varying structures in dynamic networks from local and global perspectives. The nature of this framework assumes that community structures are subject to a local evolution pattern (LEP) at each snapshot, and these LEPs are from a common global evolution pattern (GEP). The framework can synchronously detect temporal community structure, extract evolution pattern, and predict structure including communities and future snapshot links. The extensive experiments on real-world networks and artificial networks demonstrate that our proposed framework is highly effective on the tasks of dynamic network analysis.