Evolution of Networks: From Biological Nets to the Internet and WWW

By Albert-László Barabási. 2004

2,012 citations recorded by the source corpus.

Subfields: Information Systems and Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e3c580f2d1c71fa9e44b242d8fc895ca386b2177

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In Information Systems and Management

In Molecular Biology

Citations in the corpus

Mitigation of malicious attacks on networks (2011, background): “[11] Dorogovtsev SN, Mendes JFF (2003) Evolution of Networks: From Biological Nets to the Internet and WWW, (Oxford University Press, New York).”
Catastrophic cascade of failures in interdependent networks (2009, background): “After a decade of intense study on networks, almost all work done has concentrated on the limited case of a single network which does not interact with other networks [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12].”
Detecting the overlapping and hierarchical community structure in complex networks (2008, background): “The study of networks as the ”scaffold of complexity” has proved very successful to understand both the structure and the function of many natural and artificial systems [1, 2, 3, 4, 5].”
Modeling bursts and heavy tails in human dynamics (2005, background): “(D3) From (D2) we obtain the queue length distribution as P (ℓ) = 1 − ρ 1 − ρL+1 ρ ℓ , (D4) suggesting the existence of three scaling regions.”
Detecting rich-club ordering in complex networks (2006, background): “Uncovering the hidden regularities and organizational principles of networks arising in physical systems ranging from the molecular level to the scale of large communication infrastructures is the key issue for the understanding of their fabric and dynamical properties [1, 2, 3, 4, 5].”
Apollonian networks: simultaneously scale-free, small world, euclidean, space filling, and with matching graphs. (2004, background): “Next, we show that ANs display small-world effect [2,12,13].”
Survey of graph database models (2008, background): “In social networks, some measures are: distance, neighborhoods, clustering coef.cient of a vertex, clustering coef.cient of a network, betweenness, and size of giant connected components, and size distribution of .nite connected components [Dorogovtsev and Mendes 2003].”
Systematic topology analysis and generation using degree correlations (2006, methodology): “mated by the Poisson distribution: P0K(k) = e k̄/k! [11].”
Organizational network perceptions versus reality: A small world after all? ☆ (2008, background): “In a random network of n nodes and k average ties per node, the expected clustering coefficient is k/n, while the expected path length is ln(n)/ln(k) (Dorogovtsev & Mendes, 2003).”
Korean university life in a network perspective: Dynamics of a large affiliation network (2004, background): “Networks constitute, along di fferential equation models and cellular automata, a fundamental framework for analyzing a nd modeling complex systems (1; 6; 17).”
The Effects of the Social Structure of Digital Networks on Viral Marketing Performance (2008, methodology): “(2004) introduced a random viral marketing model (RVM) based on the random network model developed by Erdös and Rényi (1959) and described by Albert and Barabási (2002).”
Microscopic evolution of social networks (2008, background): “RELATED WORK Many studies on online social networks, world wide web, and biological networks focused on macroscopic properties of static networks such as degree distributions, diameter, clustering coefficient, communities, etc; work in this area includes [10, 21, 2, 18, 8, 7].”
Statistical physics of social dynamics (2007, background): “In this review we mostly discuss several different aspects of a single basic question of social dynamics: how do the interactions between social agents create order out of an initial disordered situation?”
How to calculate the fractal dimension of a complex network: the box covering algorithm (2007, background): “Complex networks are important since they describe efficiently many social, biological and communication systems [1, 2, 3, 4, 5].”
Generating Simple Random Graphs with Prescribed Degree Distribution (2006, methodology): “r1 > μR/2, (7) and introduce a new distribution R′ by defining r ′ k =  r0 + μR/2 for k = 0; r1 − μR/2 for k = 1; rk for k ≥ 2, that is, R′ is obtained by transferring the mass μR/2 from the point 1 to the point 0 in the distribution R (note that a consequence of (7) is that R must be chosen so th”