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• W2917258379 abstract "Abstract Community detection plays a significant role in understanding the essence of a network. A recently proposed algorithm Attractor, which is based on distance dynamics, can spot communities effectively, but it depends on a cohesion parameter. Moreover, no efficient way is provided to find an optimal cohesion parameter setting. In this paper, we propose a parameter-free community detection algorithm by synchronizing distances iteratively. In each iteration, the distance of each edge will change dynamically according to the effect generated by its related neighbours. Several iterations later, distances between vertices belonging to the same community will synchronize to 0, while distances between vertices not in the same community will synchronize to 1. Besides, merging and division strategies are built up in the process of community detection. Experiments on both real-world and synthetic networks demonstrate benefits of our method compared to the baseline methods." @default.
• W2917258379 created "2019-03-02" @default.
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• W2917258379 date "2019-02-23" @default.
• W2917258379 modified "2023-10-17" @default.
• W2917258379 title "Parameter-free Community Detection through Distance Dynamic Synchronization" @default.
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• W2917258379 doi "https://doi.org/10.1093/comjnl/bxy080" @default.
• W2917258379 hasPublicationYear "2019" @default.
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