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Titlebook: Complex Networks XIV; Proceedings of the 1 Andreia Sofia Teixeira,Federico Botta,Giuseppe Man Conference proceedings 2023 The Editor(s) (if

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樓主: Julienne
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發(fā)表于 2025-3-28 16:03:53 | 只看該作者
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發(fā)表于 2025-3-29 02:27:33 | 只看該作者
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發(fā)表于 2025-3-29 11:12:12 | 只看該作者
,Learned Monkeys: Emergent Properties of?Deep Reinforcement Learning Generated Networks,aracteristics of the proximity network constructed from an observational dataset. For example, our model-generated graph and the observed graph consistently showed a few members with significantly higher betweenness centrality than all other members despite each agent starting with the same parameters.
46#
發(fā)表于 2025-3-29 11:49:02 | 只看該作者
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發(fā)表于 2025-3-29 19:46:20 | 只看該作者
,CoreGDM: Geometric Deep Learning Network Decycling and?Dismantling, degree-based one. Extensive experiments on fifteen real-world networks show that CoreGDM outperforms the original GDM formulation and the other state-of-the-art algorithms, while also being more computationally efficient.
49#
發(fā)表于 2025-3-30 03:49:45 | 只看該作者
50#
發(fā)表于 2025-3-30 07:33:36 | 只看該作者
,Brain’s Dynamic Functional Organization with?Simultaneous EEG-fMRI Networks,(EEG). Recent studies have suggested a link between the dynamic functional connectivity (dFC) captured by these two modalities, but the exact relationship between their spatiotemporal organization is still unclear. Since these networks are spatially embedded, a question arises whether the topologica
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