Measuring vertex centrality using the Holevo quantity

Luca Rossi*, Andrea Torsello

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, the increasing availability of data describing the dynamics of real-world systems led to a surge of interest in the complex networks of interactions that emerge from such systems. Several measures have been introduced to analyse these networks, and among them one of the most fundamental ones is vertex centrality, which quantifies the importance of a vertex within a graph. In this paper, we propose a novel vertex centrality measure based on the quantum information theoretical concept of Holevo quantity. More specifically, we measure the importance of a vertex in terms of the variation in graph entropy before and after its removal from the graph. More specifically, we find that the centrality of a vertex v can be broken down in two parts: (1) one which is negatively correlated with the degree centrality of v, and (2) one which depends on the emergence of non-trivial structures in the graph when v is disconnected from the rest of the graph. Finally, we evaluate our centrality measure on a number of real-world as well as synthetic networks, and we compare it against a set of commonly used alternative measures.

Original languageEnglish
Title of host publicationGraph-based representations in pattern recognition : 11th IAPR-TC-15 international workshop, GbRPR 2017. Proceedings
EditorsPasquale Foggia, Cheng-Lin Liu, Mario Vento
Place of PublicationCham (CH)
PublisherSpringer
Pages154-164
Number of pages11
ISBN (Electronic)978-3-319-58961-9
ISBN (Print)978-3-319-58960-2
DOIs
Publication statusPublished - 2017
Event11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017 - Anacapri, Italy
Duration: 16 May 201718 May 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10310
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017
CountryItaly
CityAnacapri
Period16/05/1718/05/17

Keywords

  • complex networks
  • quantum Information
  • vertex centrality

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  • Research Output

    • 2 Conference contribution

    Adaptive feature selection based on the most informative graph-based features

    Bai, L., Cui, L., Rossi, L., Hancock, E. R. & Jiao, Y., 2017, Graph-based representations in pattern recognition : 11th IAPR-TC-15 international workshop, GbRPR 2017. Proceedings. Foggia, P., Liu, C-L. & Vento, M. (eds.). Cham (CH): Springer, p. 276-287 12 p. (Lecture Notes in Computer Science; vol. 10310).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
    File
  • A nested alignment graph kernel through the dynamic time warping framework

    Bai, L., Rossi, L., Cui, L. & Hancock, E. R., 2017, Graph-based representations in pattern recognition : 11th IAPR-TC-15 international workshop, GbRPR 2017. Proceedings. Foggia, P., Liu, C-L. & Vento, M. (eds.). Cham (CH): Springer, p. 59-69 11 p. (Lecture Notes in Computer Science; vol. 10310).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
    File
  • Cite this

    Rossi, L., & Torsello, A. (2017). Measuring vertex centrality using the Holevo quantity. In P. Foggia, C-L. Liu, & M. Vento (Eds.), Graph-based representations in pattern recognition : 11th IAPR-TC-15 international workshop, GbRPR 2017. Proceedings (pp. 154-164). (Lecture Notes in Computer Science; Vol. 10310). Springer. https://doi.org/10.1007/978-3-319-58961-9_14