An efficient method for mining frequent weighted subgraphs based on weighted edges
Since the GraMi algorithm was introduced to mine frequent subgraphs, many other algorithms based it have also been published. The OWGraMi (Optimized Weighted Graph Mining) published in 2022 can be considered a state-of-the-art algorithm for mining frequent subgraphs in a single large weighted graph....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-05-01
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| Series: | Journal of Information and Telecommunication |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/24751839.2025.2500132 |
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| Summary: | Since the GraMi algorithm was introduced to mine frequent subgraphs, many other algorithms based it have also been published. The OWGraMi (Optimized Weighted Graph Mining) published in 2022 can be considered a state-of-the-art algorithm for mining frequent subgraphs in a single large weighted graph. However, the OWGraMi algorithm is still limited when it only considers the weights of vertices on the graph, not edge weights. In this paper, we introduce two new contributions to further improve the OWGraMi algorithm: calculating weights for subgraphs based on their edge weights, and using these edge weights to prune the search space to increase the performance of the new weighted graph mining algorithm, named WEGM (Weighted Edge GraMi). Our new algorithm demonstrates that it outperforms OWGraMi in many evaluation criteria, such as search space, processing time and memory consumption. |
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| ISSN: | 2475-1839 2475-1847 |