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A novel approach to discover frequent weighted subgraphs using the average measure

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Abstract

Mining a weighted single large graph has recently attracted many researchers. The WeGraMi algorithm is considered the state-of-the-art among current approaches. It uses a MaxMin measure to calculate weights for all mined subgraphs. However, if all values in the domain have the same role and the user needs an average value of that domain, then we have to use another measure. In this paper, we introduce a novel algorithm called AWeGraMi (Average Weighted Graph Mining) to solve the above problem, and our method calculates the weight based on the average of all values in the domain. We also apply the MaxMin measure as an upper-bound to prune the search space. The new algorithm can mine all frequent weighted subgraphs effectively. Our experiments on the directed and undirected datasets have shown that AWeGraMi has better performance in comparison to post-processing GraMi for all three criteria: search space (the number of candidate subgraphs), running time, and memory consumption.

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Authors and Affiliations

  1. Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam

    Ngoc-Thao Le & Bac Le

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Ngoc-Thao Le & Bac Le

  3. Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam

    Ngoc-Thao Le & Bay Vo

  4. Department of Computer Engineering, Sejong University, Seoul, Republic of Korea

    Unil Yun

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  1. Ngoc-Thao Le
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  2. Bay Vo
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  3. Unil Yun
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  4. Bac Le
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Correspondence to Bac Le.

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Le, NT., Vo, B., Yun, U. et al. A novel approach to discover frequent weighted subgraphs using the average measure. Appl Intell 53, 19491–19504 (2023). https://doi.org/10.1007/s10489-023-04501-y

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