An Optimization of Closed Frequent Subgraph Mining Algorithm

Demetrovics, János; Quang, Hoang Minh; Anh, Nguyễn Việt; Thi, Vu Duc

doi:10.1515/cait-2017-0001

Cited by 6 publications

(3 citation statements)

References 9 publications

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Section: Two Phases Of Fsmmentioning

confidence: 99%

“…The problem of mining closed frequent subgraphs from a single large graph G is to find all frequent subgraphs S if there does not exist a proper supergraph S′ (of S in G ) whose support is equal to that of S (Demetrovics et al, 2017; Yan & Han, 2003). According to our survey, there is very little research on this issue (Acosta‐Mendoza et al, 2017; Bendimerad et al, 2018; Demetrovics et al, 2017; Fournier‐Viger et al, 2020; Güvenoglu & Bostanoglu, 2018; Karabadji et al, 2016; Yan & Han, 2003). In 2021, CloGraMi algorithm (Nguyen, Nguyen, et al, 2021) was proposed based on the GraMi algorithm to find all closed subgraphs in a single large graph, it also was applied two more effective strategies: the level order traversal strategy and early pruning nonclosed candidates.…”

Section: Two Phases Of Fsmmentioning

confidence: 99%

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Subgraph mining in a large graph: A review

Nguyen

Zelinka

Snášel

et al. 2022

WIREs Data Min & Knowl

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Large graphs are often used to simulate and model complex systems in various research and application fields. Because of its importance, frequent subgraph mining (FSM) in single large graphs is a vital issue, and recently, it has attracted numerous researchers, and played an important role in various tasks for both research and application purposes. FSM is aimed at finding all subgraphs whose number of appearances in a large graph is greater than or equal to a given frequency threshold. In most recent applications, the underlying graphs are very large, such as social networks, and therefore algorithms for FSM from a single large graph have been rapidly developed, but all of them have NP-hard (nondeterministic polynomial time) complexity with huge search spaces, and therefore still need a lot of time and memory to restore and process. In this article, we present an overview of problems of FSM, important phases in FSM, main groups of FSM, as well as surveying many modern applied algorithms. This includes many practical applications and is a fundamental premise for many studies in the future.

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Section: Two Phases Of Fsmmentioning

confidence: 99%

Section: Two Phases Of Fsmmentioning

confidence: 99%

Subgraph mining in a large graph: A review

Nguyen

Zelinka

Snášel

et al. 2022

WIREs Data Min & Knowl

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“…In recent years, the pervasive influence of "big data", propelled by the advancement of information technology, has permeated diverse fields, including but not limited to data mining [1][2][3], computer biology [4], environmental science [5], e-commerce [6], and social network analysis [7]. Within these domains, the analysis and extraction of concealed information from extensive datasets have become standard practices, often approached from innovative perspectives [8][9][10]. As a general data structure, the graph has found widespread application across the aforementioned fields.…”

Section: Introductionmentioning

confidence: 99%

FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP

Leng,

Li,

Bao

et al. 2024

Applied Sciences

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As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes a frequent subgraph mining (FSM) algorithm. This FSM algorithm is developed within a distributed graph iterative system, designed for the Big Cloud (BC) environment of the China Mobile Corp., and is based on the bulk synchronous parallel (BSP) model, named FSM-BC-BSP. Its aim is to address the challenge of mining frequent subgraphs within a single, large graph. This study advocates for the incorporation of a message sending and receiving mechanism to facilitate data sharing across various stages of the frequent subgraph mining algorithm. Additionally, it suggests employing a standard coded subgraph and sending it to the same node for global support calculation on the large graph. The adoption of the rightmost path expansion strategy in generating candidate subgraphs helps to mitigate the occurrence of redundant subgraphs. The use of standard coding ensures the unique identification of subgraphs, thus eliminating the need for isomorphism calculations. Support calculation is executed using the Minimum Image (MNI) measurement method, aligning with the downward closure attribute. The experimental results demonstrate the robust performance of the FSM-BC-BSP algorithm across diverse input datasets and parameter configurations. Notably, the algorithm exhibits exceptional efficacy, particularly in scenarios with low support requirements, showcasing its superior performance under such conditions.

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A Method for Closed Frequent Subgraph Mining in a Single Large Graph

Nguyen

Zelinka

et al. 2021

IEEE Access

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Mining frequent subgraphs is an interesting and important problem in the graph mining field, in that mining frequent subgraphs from a single large graph has been strongly developed, and has recently attracted many researchers. Among them, MNI-based approaches are considered as state-of-the-art, such as the GraMi algorithm. Besides frequent subgraph mining (FSM), frequent closed frequent subgraph mining was also developed. This has many practical applications and is a fundamental premise for many studies. This paper proposes the CloGraMi (Closed Frequent Subgraph Mining) algorithm based on GraMi to find all closed frequent subgraphs in a single large graph. Two effective strategies are also developed, the first one is a new level order traversal strategy to quickly determine closed subgraphs in the searching process, and the second is setting a condition for early pruning a large portion of non-closed candidates, both of them aim to reduce the running time as well as the memory requirements, improve the performance of the proposed algorithm. Our experiments are performed on five real datasets (both directed and undirected graphs) and the results show that the running time as well as the memory requirements of our algorithm are significantly better than those of the GraMi-based algorithm.INDEX TERMS data mining; frequent closed subgraph; social network; pruning strategy.

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An Optimization of Closed Frequent Subgraph Mining Algorithm

Abstract: Graph mining is a major area of interest within the field of data mining in

Cited by 6 publications

References 9 publications

Subgraph mining in a large graph: A review

Subgraph mining in a large graph: A review

FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP

A Method for Closed Frequent Subgraph Mining in a Single Large Graph

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