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- W4387143722 abstract "Community detection in time-variant networks is a challenging task. Owing to the dynamic nature of the interactions between the participant entities, the network structure tends to evolve continuously. There are two different approaches for community detection in time-variant networks, namely snapshot-based and evolutionary-based. The former technique is prone to instabilities, while the latter is expensive. This work investigates the aggregated snapshot-based technique based on a high school interaction data set. The whole time-stamped networked data is partitioned into five temporal instances. We have extended the Louvain community detection algorithm for weighted time-variant networks. On analyzing the data collected from community detection, we found that each individual instance has similar characteristics as compared to the whole network for the detected communities." @default.
- W4387143722 created "2023-09-29" @default.
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- W4387143722 date "2023-08-03" @default.
- W4387143722 modified "2023-10-18" @default.
- W4387143722 title "Community Detection in Weighted Time-Variant Social Network" @default.
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- W4387143722 doi "https://doi.org/10.1145/3607947.3608077" @default.
- W4387143722 hasPublicationYear "2023" @default.
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