Difference between revisions of "Dynamic Social Network Analysis using Latent Space Models"
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== Summary == | == Summary == | ||
This paper address the problem of [[AddressesProblem::Social Network Analysis]] using the method of [[UsesMethod::Latent Space Models]]. More specifically, it addresses the problem of network evolution in social network analysis, by modelling the way in which the friendships drift over time. It efficiently learns this even when ''n'' is large, by assuming that nodes represented by points in the latent space do not make large movements over time. Hence it is a latent space model developed for dynamic analysis of social networks to predict the future link structure of the graph. | This paper address the problem of [[AddressesProblem::Social Network Analysis]] using the method of [[UsesMethod::Latent Space Models]]. More specifically, it addresses the problem of network evolution in social network analysis, by modelling the way in which the friendships drift over time. It efficiently learns this even when ''n'' is large, by assuming that nodes represented by points in the latent space do not make large movements over time. Hence it is a latent space model developed for dynamic analysis of social networks to predict the future link structure of the graph. | ||
| + | |||
| + | This paper is generalization of static modelling in [[UsesMethod::Latent Space Models]] to Dynamic Social Networks by allowing latent coordinates to change smoothly over time, that is, between any two discretized time steps large movements of points are improbable. | ||
This technique is applied to [[UsesDataset::NIPS]] data to analysis the dynamics of network evolution. | This technique is applied to [[UsesDataset::NIPS]] data to analysis the dynamics of network evolution. | ||
| − | == | + | |
| + | == Overview of the Method == | ||
| + | Suppose that each observed link is associated with a discrete time step, then each time step produces its own graph. Further, with the Markov assumption the latent locations at the next time step are conditionally independent of locations in all other time steps given locations in current time step. This is very similar to method to [HMM] models | ||
== Learning Stage One: Linear Approximation == | == Learning Stage One: Linear Approximation == | ||
Revision as of 16:50, 1 April 2011
This is one of the paper written in course Social Media Analysis 10-802 in Spring 2011
Contents
Citation
Purnamrita Sarkar, Andrew W. Moore "Dynamic Social Network Analysis using Latent Space Models"
Online Version
Summary
This paper address the problem of Social Network Analysis using the method of Latent Space Models. More specifically, it addresses the problem of network evolution in social network analysis, by modelling the way in which the friendships drift over time. It efficiently learns this even when n is large, by assuming that nodes represented by points in the latent space do not make large movements over time. Hence it is a latent space model developed for dynamic analysis of social networks to predict the future link structure of the graph.
This paper is generalization of static modelling in Latent Space Models to Dynamic Social Networks by allowing latent coordinates to change smoothly over time, that is, between any two discretized time steps large movements of points are improbable.
This technique is applied to NIPS data to analysis the dynamics of network evolution.
Overview of the Method
Suppose that each observed link is associated with a discrete time step, then each time step produces its own graph. Further, with the Markov assumption the latent locations at the next time step are conditionally independent of locations in all other time steps given locations in current time step. This is very similar to method to [HMM] models
