Scale-free networks

By César A. Hidalgo, Albert-László Barabási. Scholarpedia, 2008

2,494 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/628e924c8fec87e190ce2e6c625a4f4afa1e696b

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In Statistical and Nonlinear Physics

Citations in the corpus

Limits of modularity maximization in community detection (2011, background): “…of modularity maximization in community detection Andrea Lancichinetti1, 2 and Santo Fortunato3, 1 1Complex Networks and Systems Lagrange Lab, Institute for Scientific Interchange, Torino, Italy 2Physics Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino,…”
Finding Statistically Significant Communities in Networks (2010, neutral): “The resulting superedges are weighted by the number of edges between 6 Check internal structure Individual cluster cleaning-up Check unions and similar modules Hierarchical analysis: building next network level Reading Initial Network MC result: final cover Cover (1, 2 ... n) realization i Reading I”
Analysis of Public Bus Transportation of a Brazilian City Based on the Theory of Complex Networks Using the P-Space (2016, background): “The random network null-hypothesis thus makes a specific prediction: degree distributions are bell-shaped normal distribution [22].”
The role of venture capital firms in Silicon Valley's complex innovation network (2009, background): “CNT points out that some nodes are more important for the resilience of the network because of the non-randomness of complex networks (Barabási & Bonabeau, 2003; Newman, 2003).”
Reinterpreting Central Place Networks Using Ideas from Fractals and Self-Organized Criticality (2006, background): “Exponential laws are always associated with randomness and disorder whereas power laws are related to structure and order (Barabäsi and Bonabeau, 2003).”
Unsupervised modeling of object categories using link analysis techniques (2008, background): “…the resulting representation is compact and efﬁcient because the graphs involved are very sparse and, like other complex networks such as those found in social networks, WWW, or epidemiology, the graph used in our approach roughly follows the power law ( i.e . it is a scale-free network) [1].”
An event-based framework for characterizing the evolutionary behavior of interaction graphs (2007, background): “Early research in this area [13, 24, 4, 5] has primarily focused on the static properties of these networks, neglecting the fact that most real-world interaction networks are dynamic in nature.”
Consensus clustering in complex networks (2012, background): “Network systems [1–8] typically display a modular organization, reﬂecting the existence of special aﬃnities among vertices in the same module, which may be a consequence of their having similar features or the same roles in the network.”
An appraisal of athlete development models through citation network analysis (2010, background): “In network analysis, the more prominent features (e.g., scholars, articles) identified by higher centrality scores are often referred to as ‘‘connectors’’ or ‘‘hubs’’ (Barabási & Bonabeau, 2003).”
How Much Language Is Enough? Theoretical and Practical Use of the Business Process Modeling Notation (2008, background): “The ranked frequency distribution of BPMN constructs generally follows an exponential (power-law) distribution, similar to long-tailed distributions that have been observed as a result of preferential attachment [12].”
A Comprehensive Survey on Community Detection With Deep Learning (2021, background): “For instance, being scale-free or having a power-law degree distribution [22], [191], such as with many social networks, can be highly problematic.”
What's in a crowd? Analysis of face-to-face behavioral networks (2010, background): “The representation of this information in terms of complex networks (Anon, 2009; Dorogovtsev and Mendes, 2003; Newman, 2003; Pastor-Satorras and Vespignani, 2004; Caldarelli, 2007; Barrat et al., 2008; Wasserman and Faust, 1994; Watts, 2007) has led to many research efforts because of the naturally ”
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning (2021, background): “These social networks present a hierarchical structure of ideation propagation, characteristic for Scale-free networks (Barabási and Bonabeau, 2003).”