Statistical mechanical study on relaxation of the bipartite constraint in community detection of graphs

2019 Fiscal Year Final Research Report

• Project/Area Number: 18K18127
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 61040:Soft computing-related
• Research Institution: National Institute of Advanced Industrial Science and Technology
• Principal Investigator: Kawamoto Tatsuro 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究員 (10791444)
• Project Period (FY): 2018-04-01 – 2020-03-31
• Keywords: グラフ分割 / ネットワーク / 統計推論

Outline of Final Research Achievements

The goal of this research was to obtain a deeper insight into inference algorithms of group structures on bipartite graphs. We initially considered to work on revealing the effects caused by relaxing the bipartite constraint on a graph. As we proceed, however, we realized a need to work on algorithmic issues on graphs and obtained several results in that direction (greedy algorithms, path-based algorithms, spectral method, etc.).

Free Research Field

情報統計力学

Academic Significance and Societal Importance of the Research Achievements

研究代表者は、グラフ推論を用いて、自由記述式アンケートを分析する方法を提案しており、本研究はそのための理論研究という位置付けであった。自由記述式アンケートは、社会調査を行う上で大変重要である。社会の実態を正確に捉えるために、将来的に本研究の成果が役立てば良いと考えている。