What is network science?

By U. Brandes, Garry Robins, Ann McCranie, Stanley Wasserman. 2013

3,187 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics, Graph Learning, Network Science & Complex Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistical and Nonlinear Physics

In Graph Learning

In Network Science & Complex Systems

Citations in the corpus

What are higher-order networks? (2021, background): “to infrastructure systems, such as transportation networks [15,202,256].”
A comprehensive review of Rasch measurement in language assessment: Recommendations and guidelines for research (2020, methodology): “Network analysis is a collection of system analytics techniques that have been developed and widely used in social sciences, physical/natural sciences, and engineering (Barabási, 2003, 2016; Easley & Kleinberg, 2010; Sayama, 2015; T a b le 2 .”
Anomaly detection in online social networks (2014, background): “Consequently, early models (reviewed by Chakrabarti and Faloutsos (2006), see also Sallaberry et al. (2013)) capture only a small number of what we now consider to be general properties of social networks Akoglu and Faloutsos (2009), while more recent models (e.g. Akoglu and Faloutsos (2009);…”
Graph Signal Processing: Overview, Challenges, and Applications (2017, background): “1) Network science: This area addresses issues such as uncovering community relations, perceived alliances, quantifying connectedness, or determining the relevance of specific agents [6], [7], [8], [9].”
Understanding network formation in strategy research: Exponential random graph models (2016, background): “To implement ERGMs, it is important to understand the dependence assumptions that define the ways in which the observed ties may be related (Brandes et al., 2013; Pattison and Robins, 2002; Robins et al., 2007a).”
Deep Learning on Graphs: A Survey (2018, neutral): “(43) actually corresponds to the second-order proximity between nodes, i.e., two nodes share similar latten representations if they have similar neighborhoods, which is a well-studied concept in network science known as collaborative ﬁltering or triangle closure [5].”
Statistical physics of vaccination (2016, methodology): “In both the SIS and the SIR model, if kc kmin, we have [282]”
Non-pharmaceutical interventions during the COVID-19 pandemic: A review (2020, background): “This result is in line with classic results from epidemic spreading on heterogenous networks which show that targeted measures aimed at isolating few central individuals are much more efficient than average measures across the whole system [142, 143].”
Communication dynamics in complex brain networks (2017, methodology): “The analysis and modelling of brain networks draw from the methods and tools of network science 16–18 .”
Economic and social consequences of human mobility restrictions under COVID-19 (2020, neutral): “We characterize daily connectivity patterns (16) through network measures (17, 18) .”