Concentric network symmetry

  title={Concentric network symmetry},
  author={Filipi Nascimento Silva and C{\'e}sar Henrique Comin and Thomas K. D. M. Peron and Francisco Aparecido Rodrigues and Cheng Ye and Richard C. Wilson and Edwin R. Hancock and Luciano da Fontoura Costa},
  journal={Inf. Sci.},

Accessibility: A Generalization of the Node Degree (A Tutorial)

The accessibility of a node is provided, which can be understood as a generalization of the concept of node degree not only to incorporate successive neighborhoods of that node, but also to reflect specific types of dynamics unfolding in the network.

A framework for evaluating complex networks measurements

This letter proposes a framework for evaluating the quality of complex-network measurements in terms of their effective resolution, degree of degeneracy and discriminability, and indicates a markedly superior performance for the latter type of mapping.

Paragraph-based representation of texts: A complex networks approach

Harmonic Complex Networks

The musical interpretations of the results include the confirmation of the more regular consonance pattern of the equal temperament, obtained at the expense of a wider range of consonances such as that obtained in the meantone temperament.

Syntonets: toward a harmony-inspired general model of complex networks

It is suggested here that syntony may provide a kind of universal complex network model, spanning the space comprised between traditional models, taking into account the consonances and dissonances between notes as defined by scale temperaments.

Accessibility and Trajectory-Based Text Characterization

This work adopt an extension to the mesoscopic approach to represent text narratives, in which only the recurrent relationships among tagged parts of speech are considered to establish connections among sequential pieces of text (e.g., paragraphs).

On the “Calligraphy” of Books

A complex network approach to assign the authorship of texts based on their mesoscopic representation, in an attempt to capture the flow of the narrative, allowed the identification of the dominant narrative structure of the studied authors.



Emergence of symmetry in complex networks.

An improved version of the Barabaśi-Albert model integrating similar linkage pattern successfully reproduces the symmetry of real networks, indicating that similar linkagepattern is the underlying ingredient that is responsible for the emergence of symmetry in complex networks.

Detecting degree symmetries in networks.

  • P. Holme
  • Mathematics, Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2006
It is found that most studied examples of degree symmetry are weakly positively degree symmetric, and the exceptions are an airport network (having a negative degree-symmetry coefficient) and one-mode projections of social affiliation networks that are rather strongly degree asymmetric.

Seeking for simplicity in complex networks

This work addresses the identification of complex networks by seeking for subgraphs whose nodes exhibit similar measurements, and paves the way for complementing the characterization of networks, including results suggesting that the protein-protein interaction networks, and to a lesser extent also the Internet, may be getting simpler over time.

Spatial Networks

  • M. Barthelemy
  • Computer Science
    Encyclopedia of Social Network Analysis and Mining
  • 2014

Networks: An Introduction

This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.

The Structure of Complex Networks: Theory and Applications

The second part of this book is devoted to the analysis of genetic, protein residue, protein-protein interaction, intercellular, ecological and socio-economic networks, including important breakthroughs as well as examples of the misuse of structural concepts.

Correlation dimension of complex networks

This measure is derived from the correlation sum of a trajectory generated by a random walker navigating the network, and extends the classical Grassberger-Procaccia algorithm to the context of complex networks.