Show simple item record

dc.contributor.authorLe, Ngoc-Thao
dc.contributor.authorVo, Bay
dc.contributor.authorNguyen, Lam B. Q.
dc.contributor.authorLe, Bac
dc.date.accessioned2022-09-23T09:18:08Z
dc.date.available2022-09-23T09:18:08Z
dc.date.issued2022
dc.identifier.citationExpert Systems with Applications. 2022, vol. 204, art. no. 117625.cs
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.urihttp://hdl.handle.net/10084/148640
dc.description.abstractRecently, the problem of mining weighted subgraphs from a weighted single graph has become a vital issue because weighted graphs are generally used to restore, simulate or monitor many complex and large systems in which each object has a different role/level. This field has attracted the attention of numerous researchers, and within related studies Weighted Graph Mining (WeGraMi) can be considered as the state-of-the-art method. However, WeGraMi lacks a strategy to prune unweighted candidate subgraphs early in the process and needs a lot of time to compute the weight for all mined frequent subgraphs. In this paper, we optimize the WeGraMi algorithm with the use of two effective strategies in a new method, which we call Optimized Weighted Graph Mining (OWGraMi). Firstly, we use a strategy to prune all frequent edges which cannot reach the weighting threshold, and with this method we can decrease the number of unweighted candidates. Secondly, we reuse the weight of parent subgraphs when computing the weight for their child subgraphs, as this can reduce the running time for the mining process. On two real graph datasets, directed as well as undirected, our experiments show that both the running time and memory requirements for OWGraMi can be reduced significantly in comparison to the original algorithm.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesExpert Systems with Applicationscs
dc.relation.urihttps://doi.org/10.1016/j.eswa.2022.117625cs
dc.rights© 2022 Elsevier Ltd. All rights reserved.cs
dc.subjectweighted subgraphcs
dc.subjectearly pruning methodscs
dc.subjectweight of subgraphcs
dc.subjectsubgraph miningcs
dc.titleOWGraMi: Efficient method for mining weighted subgraphs in a single graphcs
dc.typearticlecs
dc.identifier.doi10.1016/j.eswa.2022.117625
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume204cs
dc.description.firstpageart. no. 117625cs
dc.identifier.wos000819304100003


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record