Handbook of Graphs and Networks: From the Genome to the Internet

By Stefan Bornholdt, H. Schuster. 2003

927 citations recorded by the source corpus.

From the Publisher: This book defines the field of complex interacting networks in its infancy and presents the dynamics of networks and their structure as a key concept across disciplines. The contributions present common underlying principles of network dynamics and their theoretical description and are of interest to specialists as well as to the non-specialized reader looking for an introduction to this new exciting field. Theoretical concepts include modeling networks as dynamical systems with numerical methods and new graph theoretical approaches, but also focus on networks that change their topology as in morphogenesis and self-organization. The authors offer concepts to model network structures and dynamics, focussing on approaches applicable across disciplines.

Subfields: Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/5dae891e734c95be82f67780123427d78df4728e

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In Statistical and Nonlinear Physics

Citations in the corpus

Knowledge transfer in a tourism destination: the effects of a network structure (2009, methodology): “The rich set of metrics used today originates from the combination of those coming from the tradition of social network analysis with the outcomes of the recent work (Boccaletti et al, 2006 ; Borner, Sanyal, & Vespignani, 2007 ; Bornholdt & Schuster, 2002 ).”
Main-memory triangle computations for very large (sparse (power-law)) graphs (2008, background): “fr (1)Even though some authors make no distinction between the two notions, they are different, see for instance [12, 32].”
Coupled disease–behavior dynamics on complex networks: A review (2015, neutral): “On the other hand, more analysis and prediction methods (such as mean-field method, generation function) are also proposed to explain the transition of disease on realistic networks [27,45] and immunization strategies of spatial networks are largely identified [46].”
Handbook of Big Data (2016, background): “There are many reviews and overviews for the interested reader to learn more about graph mining; see, for example, [13,14].”
Dynamics of rumor spreading in complex networks. (2003, neutral): “glected even for finite-size systems [9].”
Information Theory of Complex Networks: On Evolution and Architectural Constraints (2004, background): “k=1 N ∑ k′=1 q(k)π(k|k′) log π(k|k′) (7) Since the conditional and joint probabilities are related through: π(k|k′) = qc(k, k ′) q(k′) (8) the conditional entropy can actually be computed in terms of the two previous distributions: Hc(q|q) = − N ∑”