Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs

dc.contributor.authorGul, Haji
dc.contributor.authorAl-Obeidat, Feras
dc.contributor.authorAmin, Adnan
dc.contributor.authorMoreira, Fernando
dc.contributor.authorHuang, Kaizhu
dc.date.accessioned2023-01-10T15:24:13Z
dc.date.available2023-01-10T15:24:13Z
dc.date.issued2022-11-15
dc.description.abstractLink prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest approaches to link prediction. The Deep Walk algorithm is a common graph embedding approach that uses pure random walking to capture network structure. In this paper, we propose an efficient model for link prediction based on a hill climbing algorithm. It is used as a cost function. The lower the cost is, the higher the accuracy for link prediction between the source and destination node will be. Unlike other algorithms that predict links based on a single feature, it takes advantage of multiple features. The proposed method has been tested over nine publicly available datasets, and its performance has been evaluated by comparing it to other frequently used indexes. Our model outperforms all of these measures, as indicated by its higher prediction accuracy.pt_PT
dc.identifier.citationGul, H., Al-Obeidat, F., Amin, A., Moreira, F., & Huang, K. (2022). Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs. Mathematics, 10(Article ID 4265), 1-15. https://doi.org/10.3390/math10224265. Repositório Institucional UPT. http://hdl.handle.net/11328/4624pt_PT
dc.identifier.doihttps://doi.org/10.3390/math10224265pt_PT
dc.identifier.issn2227-7390 (Electronic)
dc.identifier.urihttp://hdl.handle.net/11328/4624
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPI - Multidisciplinary Digital Publishing Institutept_PT
dc.rightsopen accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectComplex network analysispt_PT
dc.subjectLocal link prediction methodspt_PT
dc.subjectLink predictionpt_PT
dc.subjectComplex networkspt_PT
dc.subjectHill climbingpt_PT
dc.titleHill Climbing-Based Efficient Model for Link Prediction in Undirected Graphspt_PT
dc.typejournal articlept_PT
degois.publication.firstPage1pt_PT
degois.publication.issueArticle ID 4265pt_PT
degois.publication.lastPage15pt_PT
degois.publication.titleMathematicspt_PT
degois.publication.volume10pt_PT
dspace.entity.typePublicationen
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