The Structure and Function of Complex Networks
Структура и функция сложных сетей
2003-01-01
SCID: 54.1/pqenysne
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complex networksdegree distributionspreferential attachmentsmall-world effect
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Abstract (AI)
Abstract. Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Key Findings
1
Dynamical processes on networks are important to study alongside structure to predict system behavior.
2
Empirical studies of diverse networked systems (Internet, social, biological) motivate theoretical techniques and models for understanding networks.
3
Key structural concepts reviewed include the small-world effect, degree distributions, clustering, and network correlations.
4
Random graph models and models of network growth with preferential attachment are central frameworks for explaining observed network structures.
5
The review synthesizes methods to analyze and predict behavior of complex networks rather than introducing a single new model.
Research Object
Complex networks (networked systems such as the Internet, social networks, and biological networks)
Research Subject
Structural properties and functional behaviors of complex networks, including small-world effect, degree distributions, clustering, correlations, random graph and growth/preferential-attachment models, and dynamical processes on networks
Publication Details
Publication Date
2003-01-01
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