Modeling complex evolution of social networks in a variety of specific contexts

2016 Fiscal Year Final Research Report

• Project/Area Number: 26330352
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Web informatics, Service informatics
• Research Institution: Ryukoku University
• Principal Investigator: Kimura Masahiro 龍谷大学, 理工学部, 教授 (10396153)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 社会ネットワーク分析 / ネットワーク進化モデル / 機械学習 / データマイニング / 複雑ネットワーク科学

Outline of Final Research Achievements

For analyzing evolution of trust networks in social media sites (e.g., item review sites) that offers trust-links and activities, we have proposed A-MAE model, a novel NMF model incorporating people’s evaluation of users’ activities and TCM model with time-decay. Also, for each of the proposed models, we have constructed an efficient method of inferring the values of model parameters from observed data. Using real social media data, we have experimentally demonstrated the effectiveness of the proposed models in terms of prediction performance, and moreover we have presented a variety of applications such as revealing several characteristic properties of user behavior for trust-link creation and efficiently finding influential users.

Free Research Field

知能情報学，情報数理学