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A generic framework for mining sequences with various interestingness measures in dynamic attributed graphs.

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Title: A generic framework for mining sequences with various interestingness measures in dynamic attributed graphs.
Authors: He, Cheng1 (AUTHOR) hech66@mail2.sysu.edu.cn, Cai, Jiayu2 (AUTHOR) 22S058006@stu.hit.edu.cn, Chen, Guoting3 (AUTHOR) guoting.chen@univ-lille.fr, Gan, Wensheng4 (AUTHOR) wsgan001@gmail.com
Source: Knowledge & Information Systems. Aug2025, Vol. 67 Issue 8, p6689-6716. 28p.
Subjects: Sequential pattern mining, Time-varying networks, Data mining, Statistical measurement, Databases
Abstract: Mining various patterns in dynamic graphs is a crucial task across many domains, including social networks, web analysis, and epidemiology. This paper addresses the challenge of mining multiple attributes associated with vertices in evolving large graphs over time. Current approaches to mining attributed sequences in graph databases often overlook inter-element correlations or rely on user-defined measures to prune, limiting their universality. To address this, we present a generic framework for extracting graph sequences with various interestingness measures in dynamic attributed graphs by extending support and confidence concepts. The novel support with the anti-monotonic property reduces the search space. Additionally, we introduce adaptable support and confidence measures tailored to graph sequence mining. The proposed IS-Miner algorithm liberates the pruning strategy from specific interestingness measures. Experimental evaluations on both real-life and synthetic databases validate the efficiency and effectiveness of our approach, particularly in scenarios involving multiple attributes. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Mining various patterns in dynamic graphs is a crucial task across many domains, including social networks, web analysis, and epidemiology. This paper addresses the challenge of mining multiple attributes associated with vertices in evolving large graphs over time. Current approaches to mining attributed sequences in graph databases often overlook inter-element correlations or rely on user-defined measures to prune, limiting their universality. To address this, we present a generic framework for extracting graph sequences with various interestingness measures in dynamic attributed graphs by extending support and confidence concepts. The novel support with the anti-monotonic property reduces the search space. Additionally, we introduce adaptable support and confidence measures tailored to graph sequence mining. The proposed IS-Miner algorithm liberates the pruning strategy from specific interestingness measures. Experimental evaluations on both real-life and synthetic databases validate the efficiency and effectiveness of our approach, particularly in scenarios involving multiple attributes. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              M: 08
              Text: Aug2025
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