Concentric network symmetry

•We developed a new node-center symmetry measurement for complex networks.•The symmetry values were compared among six network models as well as four real-world networks.•By using principal component analysis we obtained distinctive signatures of symmetry for each considered network. Quantification...

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Bibliographic Details
Published in:Information sciences 2016-03, Vol.333, p.61-80
Main Authors: Silva, Filipi N., Comin, César H., Peron, Thomas K. DM, Rodrigues, Francisco A., Ye, Cheng, Wilson, Richard C., Hancock, Edwin R., da F. Costa, Luciano
Format: Article
Language:English
Subjects:
Automorphisms
Complex networks
Concentric
Graphs
Markets
Measurements
Networks
Principal component analysis
Symmetry
Topology
Transformations
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Summary:•We developed a new node-center symmetry measurement for complex networks.•The symmetry values were compared among six network models as well as four real-world networks.•By using principal component analysis we obtained distinctive signatures of symmetry for each considered network. Quantification of symmetries in complex networks is typically done globally in terms of automorphisms. Extending previous methods to locally assess the symmetry of nodes is not straightforward. Here we present a new framework to quantify the symmetries around nodes, which we call connectivity patterns. We develop two topological transformations that allow a concise characterization of the different types of symmetry appearing on networks and apply these concepts to six network models, namely the Erdős–Rényi, Barabási–Albert, random geometric graph, Waxman, Voronoi and rewired Voronoi. Real-world networks, namely the scientific areas of Wikipedia, the world-wide airport network and the street networks of Oldenburg and San Joaquin, are also analyzed in terms of the proposed symmetry measurements. Several interesting results emerge from this analysis, including the high symmetry exhibited by the Erdős–Rényi model. Additionally, we found that the proposed measurements present low correlation with other traditional metrics, such as node degree and betweenness centrality. Principal component analysis is used to combine all the results, revealing that the concepts presented here have substantial potential to also characterize networks at a global scale. We also provide a real-world application to the financial market network.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2015.11.014