Journal
INFORMATION SCIENCES
Volume 418, Issue -, Pages 575-580Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2017.08.009
Keywords
Quantitative Graph Theory; Networks; Statistics; Graphs; Data Science
Categories
Funding
- Austrian Science Funds [P26142]
- Natural Science Foundation of Tianjin [17JCQNJC00300]
- National Natural Science Foundation of China
- Austrian Science Fund (FWF) [P26142] Funding Source: Austrian Science Fund (FWF)
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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. (C) 2017 Elsevier Inc. All rights reserved.
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