By Przemyslaw A. Grabowicz, José J. Ramasco, Víctor M. Eguíluz (auth.), Animesh Mukherjee, Monojit Choudhury, Fernando Peruani, Niloy Ganguly, Bivas Mitra (eds.)
This self-contained ebook systematically explores the statistical dynamics on and of complicated networks with a unique specialize in time-varying networks. within the regularly altering smooth global, there's an pressing have to comprehend difficulties on the topic of platforms that dynamically evolve in both constitution or functionality, or either. This paintings is an try to tackle such difficulties within the framework of complicated networks.
Dynamics on and of complicated Networks, quantity 2: functions to Time-Varying Dynamical structures is a set of surveys and state-of-the-art study contributions exploring key concerns, demanding situations, and features of dynamical networks that emerge in a number of advanced platforms. towards this target, the paintings is thematically geared up into 3 major sections with the first thrust on time-varying networks: half I stories social dynamics; half II makes a speciality of group identity; and half III illustrates diffusion processes.
The contributed chapters during this quantity are meant to advertise cross-fertilization in different study parts and should be worthwhile to beginners within the box, skilled researchers, practitioners, and graduate scholars attracted to pursuing examine in dynamical networks with purposes to laptop technological know-how, statistical physics, nonlinear dynamics, linguistics, and the social sciences.
This quantity follows Dynamics On and Of advanced Networks: functions to Biology, laptop technological know-how, and the Social Sciences (2009), ISBN 978-0-8176-4750-6.
Read or Download Dynamics On and Of Complex Networks, Volume 2: Applications to Time-Varying Dynamical Systems PDF
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Additional info for Dynamics On and Of Complex Networks, Volume 2: Applications to Time-Varying Dynamical Systems
Bhattacharjee, Measurement and analysis of online social networks, in Proceedings of the 5th ACM/USENIX Internet Measurement Conference (IMC’07), San Diego, CA, 2007  M. Mitzenmacher, A brief history of generative models for power law and lognormal distributions. Internet Math. 1(2), 226–251 (2004)  M. Mitzenmacher, Editorial: the future of power law research. Internet Math. J. Newman, Clustering and preferential attachment in growing networks. Phys. Rev. J. Newman, The structure and function of complex networks.
2 0 100 1 10000 Indegree d 1 100 10000 1 Indegree 100 10000 Indegree e Obs RS RW PS CN JC 1 100 Degree 10000 1 100 Degree 10000 Fig. 5 CDF of nodes receiving new links by indegree. Plots are shown for observed data (Obs) and simulated mechanisms: random selection (RS), random 2-hop walk (RW), preferential selection (PS), common neighbors (CN), and Jaccard’s coefficient (JC). The observed data does not match any one mechanism, suggesting that different mechanisms are at play in different networks.
This result shows that the new links created in the networks cannot be explained by a preferential attachment mechanism alone. Nodes are far more likely to link to nearby nodes than preferential attachment would suggest. This result is consistent with the previous observations on static networks which showed that the clustering coefficient was significantly higher than would be predicted by preferential attachment. In the next section, we focus on how nodes choose which nearby node to link to. 5 Mechanisms Causing Proximity Bias Next, we examine network growth models that are known to have a stronger bias towards proximity than preferential attachment.
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