2013 Fiscal Year Final Research Report

Extraction of multiple communities based on information diffusion results on a large social network

Research Project

Project/Area Number	23700181
Research Category	Grant-in-Aid for Young Scientists (B)
Allocation Type	Multi-year Fund
Research Field	Intelligent informatics
Research Institution	Aoyama Gakuin University
Principal Investigator	OHARA Kouzou 青山学院大学, 理工学部, 准教授 (30294127)
Project Period (FY)	2011 – 2013
Keywords	社会ネットワーク / コミュニティ抽出 / グラフマイニング / データマイニング / 情報工学
Research Abstract	In this work, we extracted multiple communities that overlap with each other, but have interest in different things from a large social network on Twitter, a notable microblogging service. To this end, we first extracted information diffusion networks by means of tags and characteristic keywords in articles, as well as results of a topic estimation method (LDA: Latent Dirichlet Allocation). Then, those networks are integrated by means of a graph mining technique that can find frequent patterns from multiple graphs. Namely, information diffusion networks are integrated if they share common substructures whose frequency is equal to or greater than a given threshold. Furthermore, we devised a method of accurately detecting change points in information diffusion sequences around which diffusion speed has changed in order to investigate the features of resulting communities.