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Activity Number: 402
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #311419
Title: Latent Space Models for Dynamic Networks
Author(s): Daniel Sewell*+ and Yuguo Chen
Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
Keywords: Embedding ; Network data ; Markov chain Monte Carlo ; Social influence ; Visualization
Abstract:

Dynamic networks are used in a variety of fields to represent the structure and evolution of the relationships between entities. We present a model which embeds longitudinal network data as trajectories in a latent Euclidean space. A Markov chain Monte Carlo algorithm is proposed to estimate the model parameters and latent positions of the nodes in the network. The model yields meaningful visualization of dynamic networks, giving the researcher insight into the evolution and the structure, both local and global, of the network. The model handles directed or undirected edges, easily handles missing edges, and lends itself well to predicting future edges. Further, a novel approach is given to detect and visualize an attracting influence between nodes. We apply the latent space model to data collected from a Dutch classroom and a cosponsorship network collected on members of the U.S. House of Representatives, illustrating the usefulness of the model.


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