Correct filtering for subgraph isomorphism search in compressed vertex-labeled graphs

Wang, Junhu; Ren, Xuguang; Anirban, Shikha; Wu, Xin-Wen

doi:10.1016/j.ins.2019.01.036

Cited by 14 publications

(11 citation statements)

References 5 publications

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“…Generally, the frequency of a candidate subgraph is determined based on the number of appearances of this subgraph (Nguyen et al, 2020; Wang et al, 2019) in the entire large graph, and most algorithms use the DCP (Elseidy et al, 2014; N. T. Le et al, 2020; Nguyen et al, 2020) of the frequency to prune the search space.…”

Section: Mining Subgraphs On a Single Large Graphmentioning

confidence: 99%

“…The program needs time for both phases (generation and testing), in which the number of generated candidates is very large (Nguyen et al, 2020), the domain of each candidate to be tested is also very large, and thus the time needed for the two phases is very significant. The large memory requirements: the number of candidate subgraphs is huge (Nguyen et al, 2020), which the system needs to store and evaluate; and for big datasets, the domain to store each candidate is also large, and thus the mining processes consume a lot of storage space. To improve the performance of GraMi for FSM, there are many algorithms like (Elseidy et al, 2014; N.‐T. Le et al, 2020; R. Li et al, 2018; Nguyen et al, 2020; Wang et al, 2019) to reduce a large portion of redundant candidates, reduce the running time or memory requirements. In 2016, ScaleMine (Abdelhamid et al, 2016) was proposed as a parallel FSM, and then SSIGRAM (Qiao et al, 2018) was introduced in 2018 which was a new parallel approach using Spark.…”

Section: Mining Subgraphs On a Single Large Graphmentioning

confidence: 99%

“…Because of the NP‐hard complexity of (2), there are many problems to be solved to reduce the computational cost for the second phase, such as pruning the search space (N.‐T. Le et al, 2020; Nguyen et al, 2020), fast identification of isomorphisms (Wang et al, 2019), parallel processing (Abdelhamid et al, 2016; Nguyen et al, 2020; Qiao et al, 2018), and so on.…”

Section: Future Directionsmentioning

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: Mining Subgraphs On a Single Large Graphmentioning

confidence: 99%

Section: Mining Subgraphs On a Single Large Graphmentioning

confidence: 99%

Section: Future Directionsmentioning

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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“…BoostISO also groups similar query vertices which allows it to prune unfruitful matches as quickly as possible. A follow up revision of the filtering phase was made by the same authors in [90].…”

Section: Structural Graph Pattern Matchingmentioning

confidence: 99%

A Survey on Distributed Graph Pattern Matching in Massive Graphs

et al. 2021

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Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.

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“…Our main result is the experimental comparison and evaluation of the presented techniques. Nevertheless, many similar but different approaches to solving the problem, as well as the subgraph isomorphism related problems, are studied in the literature [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

An experimental evaluation of refinement techniques for the subgraph isomorphism backtracking algorithms

Mihelič

Cibej

2020

Open Computer Science

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In this paper, we study a well-known computationally hard problem, called the subgraph isomorphism problem where the goal is for a given pattern and target graphs to determine whether the pattern is a subgraph of the target graph. Numerous algorithms for solving the problem exist in the literature and most of them are based on the backtracking approach. Since straightforward backtracking is usually slow, many algorithmic refinement techniques are used in practical algorithms. The main goal of this paper is to study such refinement techniques and to determine their ability to speed up backtracking algorithms. To do this we use a methodology of experimental algorithmics. We perform an experimental evaluation of the techniques and their combinations and, hence, demonstrate their usefulness in practice.

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Correct filtering for subgraph isomorphism search in compressed vertex-labeled graphs

Cited by 14 publications

References 5 publications

Subgraph mining in a large graph: A review

Subgraph mining in a large graph: A review

A Survey on Distributed Graph Pattern Matching in Massive Graphs

An experimental evaluation of refinement techniques for the subgraph isomorphism backtracking algorithms

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