Schematization of probabilistic social network theory

2014 Fiscal Year Final Research Report

• Project/Area Number: 24700220
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Sensitivity informatics/Soft computing
• Research Institution: Yamagata University (2013-2014); Tohoku University (2012)
• Principal Investigator: YASUDA Muneki 山形大学, 理工学研究科, 准教授 (20532774)
• Project Period (FY): 2012-04-01 – 2015-03-31
• Keywords: ソーシャルネットワーク / 統計的機械学習 / データマイニング / 確率的ナウ・キャスト推定 / 統計的数理解析 / アルゴリズム / 情報統計力学

Outline of Final Research Achievements

In this research, the mathematical model, that enable us to perform complex inferences on social networks, and the algorithms, involving statistical machine learning algorithms and probabilistic inference algorithms, for the model were developed.
The proposed model and algorithms for the model were applied to solve the traffic inference problem on a real traffic network. The proposed traffic inference system allows us to reconstruct traffics on the entire network using partial information of traffics. The effectiveness of the inference system was verified by using the traffic data of Sendai-shi, Miyagi-prefecture, Japan.

Free Research Field

確率的情報処理