Scale-Free Networks: A Decade and Beyond

By Albert-László Barabási. 2009

2,093 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistical and Nonlinear Physics, 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 Molecular Biology

In Statistical and Nonlinear Physics

In Network Science & Complex Systems

Citations in the corpus

Gradient-Based Neural DAG Learning (2019, background): “Barab´asi (2009) have stated that these types of graphs have similar properties to real-world networks which can be found in many different ﬁelds, although these claims remain controversial (Clauset et al., 2009).”
Brain graphs: graphical models of the human brain connectome. (2011, background): “We can say this with some confidence because graph theory has already proven to be applicable to a considerable diversity of complex systems, including markets, ecosystems, computer circuits, and gene-gene interactomes (Barabási 2009).”
Functional Molecular Ecological Networks (2010, background): “To understand whether the identified fMENs have a network topology similar to that of other complex systems, several important general network features, such as scale free, small world, modular, and hierarchical (15, 24, 25), were examined.”
Molecular ecological network analyses (2012, neutral): “[8,53,93].”
A survey on unsupervised outlier detection in high‐dimensional numerical data (2012, background): “Hubness is a phenomenon well known in graph data analysis [69].”
Network propagation: a universal amplifier of genetic associations (2017, background): “However, the difficulty with this approach is that many genes will be near disease genes owing to the ‘small world’ property of most biological networks 28 ; that is, the property that most nodes can be reached from every other node in a small number of steps.”
Lessons from "a first-principles approach to understanding the internet's router-level topology" (2019, background): “and Beyond” [24] opens with the statement: “For decades, we tacitly assumed that the components of such complex systems as the cell, the society, or the Internet are randomly wired together”—a”
Toward link predictability of complex networks (2015, background): “U the organization of real networks is a longstanding challenge in many branches of science (1).”
Differential network biology (2012, background): “They have also been useful for the study of unifying network topologies and architectures, which link molecular biology with many other scientiﬁc disciplines that involve networks (Milo et al , 2002; Barabasi, 2009; Liu et al , 2011).”
Transcriptional Regulatory Networks in Saccharomyces cerevisiae (2009, background): “This suggests that the structure of a network is inseparably connected to its history or evolution [20].”
Mathematical Modeling of Microbial Community Dynamics: A Methodological Review (2014, background): “The degree distribution of a scale-free network follows a power law, a semi-log plot of which gives a straight line [61,62].”
Phonological Neighborhood Effects in Spoken Word Perception and Production (2016, methodology): “This approach has been used to examine complex systems in economic, biological, social, and technological domains (Barabási 2009) and has recently been used to examine semantic information in the lexicon (Hills et al. 2009) as well as phonological relationships among words in the lexicon…”