K-edge Connected Components in Large Graphs: An Empirical Analysis

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dc.contributor.authorSadri, Hanieh
dc.contributor.supervisorThomo, Alex
dc.contributor.supervisorSrinivasan, Venkatesh
dc.date.accessioned2023-12-08T22:42:27Z
dc.date.available2023-12-08T22:42:27Z
dc.date.copyright2023en_US
dc.date.issued2023-12-08
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractGraphs play a pivotal role in representing complex relationships across various domains, such as social networks and bioinformatics. Key to many applications is the identification of communities or clusters within these graphs, with k-edge-connected components emerging as an important method for finding well-connected communi- ties. Although there exist other techniques such k-plexes, k-cores, and k-trusses, they are known to have some limitations. This study delves into four existing algorithms designed for computing maximal k-edge-connected subgraphs. We conduct a thorough study of these algorithms to understand the strengths and weaknesses of each algorithm in detail and propose algorithmic refinements to optimize their performance. We provide a careful implementation of each of these algorithms, using which we analyze and compare their performance on graphs of varying sizes. Our work is the first to provide such a direct experimental comparison of these four methods. Finally, we also address an incorrect claim made in the literature about one of these algorithms.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/15693
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectGraphen_US
dc.subjectCommunity Detectionen_US
dc.subjectk-Edge-Connected Componentsen_US
dc.subjectClusteringen_US
dc.subjectWell-Connected Communitiesen_US
dc.titleK-edge Connected Components in Large Graphs: An Empirical Analysisen_US
dc.typeThesisen_US

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