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Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

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  • Costa, Luciano da Fontoura
  • Rodrigues Tognetti, Marilza A.
  • Silva, Filipi Nascimento

Abstract

Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of São Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model.

Suggested Citation

  • Costa, Luciano da Fontoura & Rodrigues Tognetti, Marilza A. & Silva, Filipi Nascimento, 2008. "Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6201-6214.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:24:p:6201-6214
    DOI: 10.1016/j.physa.2008.06.034
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    References listed on IDEAS

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    1. Cardillo, Alessio & Scellato, Salvatore & Latora, Vito, 2006. "A topological analysis of scientific coauthorship networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 372(2), pages 333-339.
    2. L. da F. Costa & L. E.C. da Rocha, 2006. "A generalized approach to complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 237-242, March.
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    Cited by:

    1. Wu, Zhenxing & Lu, Xi & Deng, Yong, 2015. "Image edge detection based on local dimension: A complex networks approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 440(C), pages 9-18.
    2. Wang, Xiaojie & Slamu, Wushour & Guo, Wenqiang & Wang, Sixiu & Ren, Yan, 2022. "A novel semi local measure of identifying influential nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    3. Silva, F.N. & Rodrigues, F.A. & Oliveira, O.N. & da F. Costa, L., 2013. "Quantifying the interdisciplinarity of scientific journals and fields," Journal of Informetrics, Elsevier, vol. 7(2), pages 469-477.

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