Comparing community structure identification
- L. Danon, A. Díaz-Guilera, J. Duch, A. Arenas
- Computer Science
- 10 May 2005
It is found that the most accurate methods tend to be more computationally expensive, and that both aspects need to be considered when choosing a method for practical purposes.
Synchronization in complex networks
- A. Arenas, A. Díaz-Guilera, J. Kurths, Y. Moreno, Changsong Zhou
- Computer Science
- 19 May 2008
Multilayer networks
- Mikko Kivelä, A. Arenas, M. Barthelemy, J. Gleeson, Y. Moreno, M. Porter
- Computer ScienceJ. Complex Networks
- 27 September 2013
This chapter shows how interconnected multilayer topology describes such networks more accurately than edge coloring does and introduces the tensor formalism used to construct them.
Community detection in complex networks using extremal optimization.
The method outperforms the optimal modularity found by the existing algorithms in the literature and is feasible to be used for the accurate identification of community structure in large complex networks.
Self-similar community structure in a network of human interactions.
- R. Guimerà, L. Danon, A. Díaz-Guilera, F. Giralt, A. Arenas
- Computer SciencePhysical review. E, Statistical, nonlinear, and…
- 22 November 2002
The results reveal the self-organization of the network into a state where the distribution of community sizes is self-similar, suggesting that a universal mechanism, responsible for emergence of scaling in other self-organized complex systems, as, for instance, river networks, could also be the underlying driving force in the formation and evolution of social networks.
Analysis of the structure of complex networks at different resolution levels
- A. Arenas, A. Fernández, S. Gómez
- Computer Science
- 23 March 2007
The proposed method allows for multiple resolution screening of the modular structure, and its application to two real social networks allows us to find the exact splits reported in the literature, as well as the substructure beyond the actual split.
Mathematical Formulation of Multilayer Networks
- M. D. Domenico, A. Solé-Ribalta, A. Arenas
- Computer Science
- 18 July 2013
This paper introduces a tensorial framework to study multilayer networks, and discusses the generalization of several important network descriptors and dynamical processes—including degree centrality, clustering coefficients, eigenvectorcentrality, modularity, von Neumann entropy, and diffusion—for this framework.
Models of social networks based on social distance attachment.
- M. Boguñá, R. Pastor-Satorras, A. Díaz-Guilera, A. Arenas
- Computer SciencePhysical review. E, Statistical, nonlinear, and…
- 22 November 2004
Analytical results are derived, showing that the proposed class of models of social network formation reproduces the main statistical characteristics of real social networks: large clustering coefficient, positive degree correlations, and the emergence of a hierarchy of communities.
Structural reducibility of multilayer networks
- M. De Domenico, V. Nicosia, A. Arenas, V. Latora
- Computer ScienceNature Communications
- 23 April 2015
A method based on quantum theory is presented to identify the minimal configuration of layers to retain in multilayer networks, used to capture the structure of complex systems with different types of interactions, but often contain redundant information.
On the dynamical interplay between awareness and epidemic spreading in multiplex networks
- C. Granell, S. Gómez, A. Arenas
- MathematicsPhysical Review Letters
- 18 June 2013
The analysis of the interrelation between two processes accounting for the spreading of an epidemic, and the information awareness to prevent its infection, on top of multiplex networks reveals the phase diagram of the incidence of the epidemics and allows the evolution of the epidemic threshold depending on the topological structure of the multiplex and the inter correlation with the awareness process.
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