Quantitative Graph Theory: A new branch of graph theory and in network science

TL;DR

Quantitative graph theory introduces a measurement-based approach to analyze complex networks using numerical invariants, allowing for probabilistic methods and complementing classical deterministic graph theory within network science.

Contribution

It establishes quantitative graph theory as a new branch focusing on structural quantification of networks through numerical invariants and probabilistic methods.

Findings

Provides a framework for numerical invariants in network analysis

Demonstrates applications in network science contexts

Highlights differences from classical graph theory

Abstract

In this paper, we describe {\sc quantitative graph theory} and argue it is a new graph-theoretical branch in network science, however, with 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 {\it 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.