This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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379
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Type:
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Invited
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Date/Time:
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Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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General Methodology
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Abstract - #306312 |
Title:
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Probabilistic Modeling of Dynamic Networks Using Latent Space Models
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Author(s):
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Purnamrita Sarkar*+ and Andrew Moore
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Companies:
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Carnegie Mellon University and Google
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Address:
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, , ,
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Keywords:
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Latent Space Models ;
Dynamic Network
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Abstract:
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We generalize the successful static model of relationships (Raftery et al.) into a dynamic model that accounts for friendships drifting over time. We show how to make it tractable (sub-quadratic in number of entities) to learn such models from data. We achieve this by using appropriate kernel functions for similarity in latent space, the use of low dimensional kd-trees, a new efficient dynamic adaptation of multidimensional scaling for first pass of approximate projection of entities into latent space, and an efficient conjugate gradient update rule for non-linear local optimization in which amortized time per entity during an update is O(log n). In addition to large real-world social networks, we also successfully modeled the occurrences of Salmonella outbreaks in USDA-controlled food processing establishments as a network evolving over time.
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