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The structure and function of complex networks

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arxiv cond-mat/0303516 v1 pith:PJKKVZ72 submitted 2003-03-25 cond-mat.stat-mech cond-mat.dis-nn

classification cond-mat.stat-mechcond-mat.dis-nn
keywords networksmodelsnetworksystemsattachmentbehaviorbiologicalclustering
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Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

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  1. Generalized Schr\"odinger Bridge on Graphs

    cs.LG 2026-02 conditional novelty 6.0 of 10

    GSBoG learns topology-respecting controlled CTMC policies for graph transport that match endpoint distributions and optimize running costs via a data-driven IPF and temporal-difference training scheme.

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