Identification and Prediction of Complex Network Evolution

2015 Fiscal Year Final Research Report

• Project/Area Number: 24650116
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Sensitivity informatics/Soft computing
• Research Institution: Tokyo University of Science (2014-2015); Saitama University (2012-2013)
• Principal Investigator: Ikeguchi Tohru 東京理科大学, 工学部, 教授 (30222863)
• Project Period (FY): 2012-04-01 – 2016-03-31
• Keywords: 複雑ネットワーク / 時間発展 / 時系列 / 予測 / 状態同定

Outline of Final Research Achievements

We propose a new framework to analyze characteristics of temporal networks and to predict how the temporal networks evolve. Using the theory and techniques of nonlinear time series analysis, we first show that the proposed method in the framework can effectively transform complex networks to time series. The results show that we can analyze the temporal evolution of complex networks by the time series analysis theory. In addition, we also analyze real temporal network data (sending and receiving records of electronic mails in a private company) and model its statistical behavior. Analyzing the real data of the temporal networks, we discovered that the records have several interesting temporal features. Then, we propose m The results show that our model can reproduce several characteristics of the real network data.

Free Research Field

非線形数理工学