Deciphering the global organization of clustering in real complex networks
| Autor: | M. Ángeles Serrano, Pol Colomer-de-Simón, Marián Boguñá, Mariano G. Beiró, J. Ignacio Alvarez-Hamelin |
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| Přispěvatelé: | Universitat de Barcelona |
| Rok vydání: | 2013 |
| Předmět: |
FOS: Computer and information sciences
Physics - Physics and Society Critical phenomena (Physics) Theoretical computer science Computer science Property (programming) Ciencias Físicas Complex networks FOS: Physical sciences Physics and Society (physics.soc-ph) Article purl.org/becyt/ford/1 [https] Set (abstract data type) Exponential random graph models Cluster Analysis Computer Simulation Cluster analysis Física estadística Computer networks Social and Information Networks (cs.SI) Models Statistical Multidisciplinary Spectrum (functional analysis) Computer Science - Social and Information Networks Nonlinear phenomena purl.org/becyt/ford/1.3 [https] Disordered Systems and Neural Networks (cond-mat.dis-nn) Condensed Matter - Disordered Systems and Neural Networks Complex network Degree distribution Graph Phase transitions and critical phenomena Xarxes d'ordinadors Fenòmens crítics (Física) Statistical physics CIENCIAS NATURALES Y EXACTAS Física de los Materiales Condensados |
| Zdroj: | Dipòsit Digital de la UB Universidad de Barcelona Recercat. Dipósit de la Recerca de Catalunya instname CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET Scientific Reports |
| DOI: | 10.48550/arxiv.1306.0112 |
| Popis: | We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles. Fil: Colomer de Simón, Pol. Universidad de Barcelona; España Fil: Serrano, María de Los Angeles. Universidad de Barcelona; España Fil: Beiro, Mariano Gastón. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; Argentina Fil: Alvarez Hamelin, Jose Ignacio. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; Argentina Fil: Boguñá, Marián. Universidad de Barcelona; España |
| Databáze: | OpenAIRE |
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