Rethinking Centrality: The Role of Dynamical Processes in Social Network Analysis
Rumi Ghosh,Kristina Lerman +1 more
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TLDR
It is demonstrated, by ranking users in online social networks used for broadcasting information, that non-conservative Alpha-Centrality generally leads to a better agreement with an empirical ranking scheme than the conservative PageRank.Abstract:
Many popular measures used in social network analysis, including centrality, are based on the random walk. The random walk is a model of a stochastic process where a node interacts with one other node at a time. However, the random walk may not be appropriate for modeling social phenomena, including epidemics and information diffusion, in which one node may interact with many others at the same time, for example, by broadcasting the virus or information to its neighbors. To produce meaningful results, social network analysis algorithms have to take into account the nature of interactions between the nodes.
In this paper we classify dynamical processes as conservative and non-conservative and relate them to well-known measures of centrality used in network analysis: PageRank and Alpha-Centrality. We demonstrate, by ranking users in online social networks used for broadcasting information, that non-conservative Alpha-Centrality generally leads to a better agreement with an empirical ranking scheme than the conservative PageRank.read more
Citations
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Journal ArticleDOI
Random walks and diffusion on networks
TL;DR: The theory and applications of random walks on networks are surveyed, restricting ourselves to simple cases of single and non-adaptive random walkers, and three main types are distinguished: discrete-time random walks, node-centric continuous-timerandom walks, and edge-centric Continuous-Time random walks.
Journal ArticleDOI
Random walks and diffusion on networks
TL;DR: Random walks have been studied for many decades on both regular lattices and (especially in the last couple of decades) on networks with a variety of structures as discussed by the authors, and they are one of the most fundamental types of stochastic processes; can be used to model numerous phenomena, including diffusion, interactions, and opinions among humans and animals; and can extract information about important entities or dense groups of entities in networks.
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Topological data analysis of contagion maps for examining spreading processes on networks
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Mean Curvature, Threshold Dynamics, and Phase Field Theory on Finite Graphs
TL;DR: In this article, the authors derived a graph curvature from the graph cut function, the natural graph counterpart of total variation (perimeter), and showed that the graph MBO scheme converges to a stationary state in a finite number of iterations.
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Simulation of rerouting phenomena in Dynamic Traffic Assignment with the Information Comply Model
Rafał Kucharski,Guido Gentile +1 more
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References
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Rethinking Centrality: The Role of Dynamical Processes in Social Network Analysis
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