2022 Fiscal Year Final Research Report
Overlappping Community discovery based on Line Graph
| Project/Area Number |
18K11436
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Nara Women's University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Keywords | 情報工学 / 機械学習 / 社会ネットワーク分析 / コミュニティ発見 |
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| Outline of Final Research Achievements |
In order to cope with increasing quantity and variety of data, it is important to develop information technology which enables effective use of network data. We have developed a method for overlappping community discovery based on the line graph in graph theory. In the developed method, the relationship among nodes is represented as a line graph in graph theory, and the weights of the line graph is defined based on the weights in the original network. Under the framework of optimization learning, we have developed a overlapping community discovery algorithm based on the representation matrix of the given network. The algorithm has been implemented as a prototype system, and experiments over the prototype system were conducted over several real-world datasets. The results indicate the effectiveness of the developed learning method.
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| Free Research Field |
知能情報学
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| Academic Significance and Societal Importance of the Research Achievements |
従来のアプローチではノードをそれぞれコミュニティに割り当てるノード分割を行うため，各ノードはひとつのコミュニティにしか所属できないという課題があった．しかし，実世界のネットワークにおいては，ノードに対応するユーザは関心や興味に応じて複数のコミュニティに所属することも多い．そこで，本研究ではノードやメッセージなどの内容ではなくネットワークの構造に基づいてリンクをコミュニティに割り当て，リンクの端点となるノードは接続するリンクが割り当てられたコミュニティにそれぞれ所属するとみなすことにより，ネットワークのリンク分割に基づく多重コミュニティ発見の実現に取り組んだ．
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