## Abstract

In this paper, we describe some highlights of the new branch QUANTITATIVE GRAPH THEORY and explain its significant different features compared to classical graph theory. The main goal of quantitative graph theory is the structural quantification of information contained in complex networks by employing a measurement approach based on numerical invariants and comparisons. Furthermore, the methods as well as the networks do not need to be deterministic but can be statistic. As such this complements the field of classical graph theory, which is descriptive and deterministic in nature. We provide examples of how quantitative graph theory can be used for novel applications in the context of the overarching concept network science.

Original language | English |
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Pages (from-to) | 575-580 |

Number of pages | 6 |

Journal | Information Sciences |

Volume | 418-419 |

DOIs | |

Publication status | Published - 1 Dec 2017 |

Publication type | A1 Journal article-refereed |

## Keywords

- Data Science
- Graphs
- Networks
- Quantitative Graph Theory
- Statistics

## Publication forum classification

- Publication forum level 1

## ASJC Scopus subject areas

- Control and Systems Engineering
- Theoretical Computer Science
- Software
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence