Titlebook: Complex Networks & Their Applications XII; Proceedings of The T Hocine Cherifi,Luis M. Rocha,Murat Donduran Conference proceedings 2024 The

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https://doi.org/10.1007/88-470-0382-2ry clique detected by traditional algorithms truly satisfies the sociological assumption above. Informally speaking, the approach presented in this paper assumes that each pair of clique nodes must be closer to each other and other clique nodes than to non-clique nodes. Using experiments with weight

Pituitary-Gland

https://doi.org/10.1007/88-470-0382-2equently, we evaluate established dynamic community detection methods to uncover limitations that may not be evident in snapshots with slowly evolving communities. While no method emerges as a clear winner, we observe notable differences among them.

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labile

https://doi.org/10.1007/978-3-8348-9399-4to as social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed. We develop two hierarchical network autocorrelation models to represent peer effects between hospitals when modeling individual outcomes of the patients who attend those

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Marina V. Plekhanova,Guzel D. Baybulatovay partitions. It seems obvious that isolating high-modularity communities is a good way to prevent the spread of cascading failures. Here we develop a heuristic approach informed by Moore-Shannon network reliability that focuses on dynamics rather than topology. It defines communities directly in te

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Murat Ad?var,Youssef N. Raffoulent a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly improve the quality of a non-negative matrix factorization. The results parallel those in the spectral graph clustering work of [.], where the authors proved adjacency spectral embedding (ASE) spectral clustering was

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Aphorism