2026-09-20T02:10:14Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/122154 2024-03-05 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Warnow, Tandy 2023-12 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01 The student, Seyedeh Yasamin Tabatabaee, accepted the attached license on 2023-11-29 at 10:04. The student, Seyedeh Yasamin Tabatabaee, submitted this Thesis for approval on 2023-11-29 at 10:32. This Thesis was approved for publication on 2023-12-04 at 13:13. DSpace SAF Submission Ingestion Package generated from Vireo submission #20056 on 2024-03-01 at 13:31:33 Improving the accuracy of community detection methods using connectivity modifier Tabatabaee, Seyedeh Yasamin Community Detection Clustering Connectivity Leiden Lfr Graphs 2023-12-04 Community detection algorithms are commonly used to recover the community structure of complex networks. To evaluate the accuracy of these algorithms and compare them against each other, one would need to apply them to networks with known community structure. However, the community structure of real-world networks is usually not known, and hence synthetic networks with ground-truth communities are used for benchmarking these algorithms. The most widely adopted synthetic networks for the evaluation of community detection methods are the Lancichinetti-Fortunato-Radicchi (LFR) benchmark graphs. Here we develop a pipeline for creating LFR graphs that emulate the characteristics of given real-world networks and their clusterings. While our study shows that these LFR graphs almost perfectly match some characteristics of the real-world networks they attempt to emulate, there are striking differences among their other properties. We also evaluate the recently introduced Connectivity Modifier (CM) algorithm, a meta-method for ensuring well-connectedness of clusters outputted by community detection methods, on these empirical networks and LFR graphs. Our results show that while CM reduces node coverage, it improves the accuracy of Leiden algorithm optimizing modularity or the Constant Potts model (CPM) in many model conditions. Text eng https://hdl.handle.net/2142/122154 Copyright 2023 Seyedeh Yasamin Tabatabaee M.S. Thesis Computer Science University of Illinois at Urbana-Champaign Computer Science