Research Output per year

### 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 language | English |
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Title of host publication | Graph-based representations in pattern recognition : 11th IAPR-TC-15 international workshop, GbRPR 2017. Proceedings |

Editors | Pasquale Foggia, Cheng-Lin Liu, Mario Vento |

Place of Publication | Cham (CH) |

Publisher | Springer |

Pages | 154-164 |

Number of pages | 11 |

ISBN (Electronic) | 978-3-319-58961-9 |

ISBN (Print) | 978-3-319-58960-2 |

DOIs | |

Publication status | Published - 2017 |

Event | 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017 - Anacapri, Italy Duration: 16 May 2017 → 18 May 2017 |

### Publication series

Name | Lecture Notes in Computer Science |
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Publisher | Springer |

Volume | 10310 |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Conference

Conference | 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017 |
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Country | Italy |

City | Anacapri |

Period | 16/05/17 → 18/05/17 |

### Keywords

- complex networks
- quantum Information
- vertex centrality

## Fingerprint Dive into the research topics of 'Measuring vertex centrality using the Holevo quantity'. Together they form a unique fingerprint.

## 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 proceeding › Conference contribution

## 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 proceeding › Conference contribution

## Cite this

*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