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Dynamical Processes on Complex Networks
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  • Cited by 925
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Godrèche, C and Luck, J M 2008. A record-driven growth process. Journal of Statistical Mechanics: Theory and Experiment, Vol. 2008, Issue. 11, p. P11006.

    Schweitzer, Frank Fagiolo, Giorgio Sornette, Didier Vega-Redondo, Fernando Vespignani, Alessandro and White, Douglas R. 2009. Economic Networks: The New Challenges. Science, Vol. 325, Issue. 5939, p. 422.

    Ángeles Serrano, M Klemm, Konstantin Vazquez, Federico Eguíluz, Víctor M and San Miguel, Maxi 2009. Conservation laws for voter-like models on random directed networks. Journal of Statistical Mechanics: Theory and Experiment, Vol. 2009, Issue. 10, p. P10024.

    Masuda, Naoki 2009. Immunization of networks with community structure. New Journal of Physics, Vol. 11, Issue. 12, p. 123018.

    Caccioli, Fabio and Dall’Asta, Luca 2009. Non-equilibrium mean-field theories on scale-free networks. Journal of Statistical Mechanics: Theory and Experiment, Vol. 2009, Issue. 10, p. P10004.

    Sanders, David P. 2009. Exact encounter times for many random walkers on regular and complex networks. Physical Review E, Vol. 80, Issue. 3,

    Tejedor, V. Bénichou, O. and Voituriez, R. 2009. Global mean first-passage times of random walks on complex networks. Physical Review E, Vol. 80, Issue. 6,

    Payne, Joshua L. Dodds, Peter Sheridan and Eppstein, Margaret J. 2009. Information cascades on degree-correlated random networks. Physical Review E, Vol. 80, Issue. 2,

    Baronchelli, Andrea Barrat, Alain and Pastor-Satorras, Romualdo 2009. Glass transition and random walks on complex energy landscapes. Physical Review E, Vol. 80, Issue. 2,

    Vespignani, Alessandro 2009. Predicting the Behavior of Techno-Social Systems. Science, Vol. 325, Issue. 5939, p. 425.


    Buzna, Lubos Lozano, Sergi and Díaz-Guilera, Albert 2009. Synchronization in symmetric bipolar population networks. Physical Review E, Vol. 80, Issue. 6,

    Godrèche, C. Grandclaude, H. and Luck, J. M. 2009. Finite-Time Fluctuations in the Degree Statistics of Growing Networks. Journal of Statistical Physics, Vol. 137, Issue. 5-6, p. 1117.

    Ramasco, José J. Colizza, Vittoria and Panzarasa, Pietro 2009. Complex Sciences. Vol. 4, Issue. , p. 680.

    Jackson, Matthew O. 2009. Genetic influences on social network characteristics. Proceedings of the National Academy of Sciences, Vol. 106, Issue. 6, p. 1687.

    Anand, Kartik and Bianconi, Ginestra 2009. Entropy measures for networks: Toward an information theory of complex topologies. Physical Review E, Vol. 80, Issue. 4,

    Pugliese, E. and Castellano, C. 2009. Heterogeneous pair approximation for voter models on networks. EPL (Europhysics Letters), Vol. 88, Issue. 5, p. 58004.

    Mandrà, Salvatore Fortunato, Santo and Castellano, Claudio 2009. Coevolution of Glauber-like Ising dynamics and topology. Physical Review E, Vol. 80, Issue. 5,

    Garlaschelli, Diego 2009. The weighted random graph model. New Journal of Physics, Vol. 11, Issue. 7, p. 073005.

    Sander, Renan S de Oliveira, Marcelo M and Ferreira, Silvio C 2009. Quasi-stationary simulations of the directed percolation universality class ind= 3 dimensions. Journal of Statistical Mechanics: Theory and Experiment, Vol. 2009, Issue. 08, p. P08011.

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Book description

The availability of large data sets has allowed researchers to uncover complex properties such as large-scale fluctuations and heterogeneities in many networks, leading to the breakdown of standard theoretical frameworks and models. Until recently these systems were considered as haphazard sets of points and connections. Recent advances have generated a vigorous research effort in understanding the effect of complex connectivity patterns on dynamical phenomena. This book presents a comprehensive account of these effects. A vast number of systems, from the brain to ecosystems, power grids and the internet, can be represented as large complex networks. This book will interest graduate students and researchers in many disciplines, from physics and statistical mechanics to mathematical biology and information science. Its modular approach allows readers to readily access the sections of most interest to them, and complicated maths is avoided so the text can be easily followed by non-experts in the subject.


Review of the hardback:'With a focus in dynamical processes, this book is an excellent introduction … on the statistical mechanics approach of networks … Not only [does] it consist of a wide array of commonly used techniques in the discipline but also it provides multitudes of the techniques' applied examples … this book could serve as an introduction book and a reference to new-to-the-topic JASSS readers. To conclude, I believe that this book has contributed another step in integrating the vast multidisciplinary approaches in network science.'

Source: Journal of Artificial Societies and Social Simulation

Review of the hardback:'… the book does a terrific and admirable job at putting some order into the wealth of research that has emerged during the last decade … a fantastic resource book on dynamical processes on complex networks, and its wide scope promises to keep it relevant for several years to come.'

Source: Journal of Statistical Physics

Review of the hardback:'… a very useful book that fills an important gap in the market of books on networks … I will be opening this book whenever I want to start modelling a dynamical process on a network.'

Source: Contemporary Physics

'The book does a remarkably good job in getting to the mathematical foundations of dynamical processes and complex networks. Hence, it should belong in the bookshelf of any sociologist who is seriously interested in complex and dynamic networks. It gives a great overview of techniques in the field and provides the mathematical depth one wishes for in a manner sociologists can understand. In sum, the book has potential to become a reference like Wasserman and Faust (1994) and offers significant (technical) value to sociologists who seriously want to get into the mathematics behind dynamical processes and complex networks.'

Thomas U. Grund Source: Journal of Mathematical Sociology

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