Fast and scalable algorithms for mining subgraphs in a single large graph

Nguyen, Lam B. Q.; Vo, Bay; Le, Ngoc-Thao; Snåšel, Václav; Zelinka, Ivan

doi:10.1016/j.engappai.2020.103539

Cited by 18 publications

(97 citation statements)

References 31 publications

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“…In Figure 2, node v0 (of subgraph S) has a domain including nodes {u0, u2, u6, u9} (on large graph G). Proposition 1 [33], [34]. Let S = (VS, ES, LS) be a subgraph of a graph G = (V, E, L), u ∈ V and v ∈ VS. An assignment of a node u is a valid assignment in the domain of v if there exists an isomorphism I that u is assignable to v, otherwise u is an invalid assignment.…”

Section: Definitions Propositions and Problem Statementmentioning

confidence: 99%

“…In 2014, GraMi [30], [31] was proposed as a novel approach to efficiently mine frequent subgraphs [30]. Most methods for subgraph mining in a large graph G work by searching and counting the number of isomorphisms of a subgraph S in G [30], [32], [33], [34], if this number is not less than a given frequency threshold t, this means S is a frequent subgraph. These subgraph mining algorithms suffer two main costs: (1) generating candidates [33] and (2) checking candidates' isomorphisms [21], [35].…”

Section: Introductionmentioning

confidence: 99%

“…In 2020, we improved GraMi to be able to find the supports for frequent subgraphs, with an approach called SuGraMi [33]. Because of finding just enough isomorphisms to determine that a candidate subgraph S is frequent, GraMi can only find all frequent subgraphs in a graph dataset, but not their support.…”

Section: Introductionmentioning

confidence: 99%

“…Because of finding just enough isomorphisms to determine that a candidate subgraph S is frequent, GraMi can only find all frequent subgraphs in a graph dataset, but not their support. In 2020, we improved GraMi to be able to find the supports for frequent subgraphs, with an approach called SuGraMi [33]. Although SuGraMi can find all frequent subgraphs with their useful supports, they are not closed frequent subgraphs yet.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Definitions Propositions and Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Nguyen

Zelinka

et al. 2021

IEEE Access

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“…Many previous large graph networks contain only edge structures, and do not correspond to unique vertex IDs. Many frequent subgraph mining experiments [22] use randomly generated node labels. Here we also use this method in the Deezer-HU dataset, because it does not contain any node labels; other datasets contain node labels.…”

Section: A Datasets and Comparison Algorithmmentioning

confidence: 99%

Large Graph Sampling Algorithm for Frequent Subgraph Mining

Zheng

Wang

2021

IEEE Access

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Large graph networks frequently appear in the latest applications. Their graph structures are very large, and the interaction among the vertices makes it difficult to split the structures into separate multiple structures, thus increasing the difficulty of frequent subgraph mining. The process of calculating subgraph isomorphism often requires many calculations. Reducing the unessential structure of the graph is an effective method to improve the efficiency. Therefore, we propose a large graph sampling algorithm (RASI) based on random areas selection sampling and incorporate graph induction techniques to reduce the structure of the original graph. In addition, we find that constraining the weight of the number of vertices in the entire graph is essential to reduce the calculation of subgraph isomorphisms. This parameter is constrained in the sampling process to improve the efficiency of frequent subgraph mining. Experimental results show that RASI has more stable performance and performs better than other sampling algorithms in non-connected graphs. Mining frequent subgraphs by graph sampling can significantly improve the efficiency of mining, and the number of subgraphs before and after sampling is very similar.

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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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Fast and scalable algorithms for mining subgraphs in a single large graph

Cited by 18 publications

References 31 publications

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

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

Large Graph Sampling Algorithm for Frequent Subgraph Mining

Subgraph mining in a large graph: A review

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