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- W3189705032 abstract "Community detection is an important problem in network science that discovers highly clustered groups of nodes having similar properties. Label propagation algorithm (LPA) is one of the popular clustering techniques that has attracted much attention due to its efficiency and non-dependence on parameters. Despite its advantages, an important limitation of LPA is the randomness in grouping nodes that leads to instability and the formation of large communities. In this study, we propose four variants based on LPA to overcome the random community allocation problem, henceforth generating better clusters. These variants utilize link strength and node attribute information to enhance the quality of detected communities. Furthermore, the proposed variants require no parameter for categorical attributes and only one parameter (constant for all the networks) for continuous attributes. Finally, the best results are obtained by Variant I having a maximum Normalized Mutual Information score of 0.86, 0.88 on the two synthetic networks." @default.
- W3189705032 created "2021-08-16" @default.
- W3189705032 creator A5008751557 @default.
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- W3189705032 date "2021-11-01" @default.
- W3189705032 modified "2023-10-12" @default.
- W3189705032 title "A modified label propagation algorithm for community detection in attributed networks" @default.
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- W3189705032 doi "https://doi.org/10.1016/j.jjimei.2021.100030" @default.
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