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. |
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| 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] |
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| Database: | Engineering Source |
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