Titlebook: Complex Networks IV; Proceedings of the 4 Gourab Ghoshal,Julia Poncela-Casasnovas,Robert Tol Conference proceedings 2013 Springer-Verlag Be

OVERT

Ka?s Ammari,Fathi Hassine,Luc Robbianoinion of any other agent. That is, the ”social network” of the simulated agents is a . graph. In contrast, in this paper we report on the results from a series of empirical experiments where the social network of the agent population is non-trivially structured, using the stochastic network construc

Inelasticity

魔鬼在游行

Conference proceedings 2013ensive computing resources has facilitated studies ranging from?large scale empirical analysis of networks in the real world, to the development?of theoretical models and tools to explore the various properties of these systems.?The study of networks is broadly interdisciplinary and central developm

Ccu106

1860-949X rld, to the development?of theoretical models and tools to explore the various properties of these systems.?The study of networks is broadly interdisciplinary and central developm978-3-642-44841-6978-3-642-36844-8Series ISSN 1860-949X Series E-ISSN 1860-9503

oxidant

指數(shù)

Detecting Social Capitalists on Twitter Using Similarity Measures,t these networks, the connections between users can be easily represented using (directed) graphs. In this paper, we are mainly focused on two different aspects of social network analysis. First, our goal is to find an efficient and high-level way to store and process a social network graph, using r

使堅硬

波動

Social Achievement and Centrality in MathOverflow,r a period of two and a half years.We describe relevant aspects associated with its behaviour as a result of the dynamics arisen from users participation and contribution, such as the existence of clusters, rich–club and collaborative properties within the network.We examine, in particular, the rela

Inertia

Analysis of Communities Evolution in Dynamic Social Networks,raphs named as network snapshots.We introduce a distance measure between static graphs to study similarity among network snapshots and to detect outlier events. To find a detailed structure within each network snapshot we used a modularity maximization algorithm based on a fast greedy search extende

返老還童

Entropy Production in Stationary Social Networks,ate. Such stationary conditions may have different dynamical properties. For example, if each message in a network is followed by a reply in opposite direction, the dynamics is locally balanced. Otherwise, if messages are ignored or forwarded to a different user, one may reach a stationary state wit