Analysis method for time-evolving networks and its application to high resolution data analysis

2016 Fiscal Year Final Research Report

• Project/Area Number: 26880020
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Single-year Grants
• Research Field: Soft computing
• Research Institution: Tokyo University of Science
• Principal Investigator: Shimada Yutaka 東京理科大学, 工学部情報工学科, 助教 (50734414)
• Project Period (FY): 2014-08-29 – 2016-03-31
• Keywords: 複雑ネットワーク / グラフ間距離 / グラフラプラシアン / テンポラル・ネットワーク

Outline of Final Research Achievements

The internet, neuronal networks, sending/receiving relationship of e-mails, and human contacts are described and analyzed as complex networks. They usually evolve with time, changing their connections between vertices, and it is highly important to analyze their underlying spatio-temporal properties in observed data.
We have focused on networks whose structures vary with time, and proposed a distance that can appropriately evaluate the amount of change from the perspective of spatio-temporal structures of networks. We applied our method to mathematical models and real network data, and showed that the proposed distance can detect the properties of networks that cannot be detected by the conventional distances.

Free Research Field

感性情報学・ソフトコンピューティング