2022 Fiscal Year Final Research Report
Construction of a framework for searching similar graphs from graph databases
| Project/Area Number |
20K11835
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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 60080:Database-related
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| Research Institution | Kwansei Gakuin University |
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Principal Investigator |
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | グラフ / グラフ検索 / 類似グラフ / グラフ編集距離 / 部分グラフ同型判定 |
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| Outline of Final Research Achievements |
The first contribution of this study is to design the framework for graph search. It is used as our common software foundation for solving graph search problems such as supergraph search, similar supergraph search, and subgraph search. The second one is that we newly defined the similar supergraph search problem and proposed the method for solving it. The similar supergraph search problem is the problem of obtaining graphs similar to subgraphs of a query graph q from a database of many graphs. Although both obtaining subgraphs and computing graph similarity are NP-complete, we achieved a search method that works fast.
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| Free Research Field |
データベース
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| Academic Significance and Societal Importance of the Research Achievements |
グラフ形式で蓄積された大量のデータを利活用するには，所望のデータを素早く得るための検索技術が必要となる．部分グラフ同型判定問題はNP完全であるので，大量のグラフデータに対して，２つのグラフの間の部分グラフ同型判定問題を複数回解くことは適切ではない．本研究で実現した検索技術は，そのような問題を解かず，データベースの複数のグラフとクエリグラフの間のグラフ同型判定問題を同時に解くことができる．また，部分グラフ検索，包摂グラフ検索，類似包摂グラフ検索に対応できるようにソフトウェアをデザインしており，様々な検索問題を解くために容易に拡張できる．
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