Scholarly Works - Conferences
Conference
Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference
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December 1, 2009
This article discusses a latent variable model for inference and prediction of symmetric relational data. The model, based on the idea of the eigenvalue decomposition, represents the relationship between two nodes as the weighted inner-product of node-spec ...
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Conference
Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference
·
January 1, 2008
This article discusses a latent variable model for inference and prediction of symmetric relational data. The model, based on the idea of the eigenvalue decomposition, represents the relationship between two nodes as the weighted inner-product of node-spec ...
Cite
Conference
Advances in Neural Information Processing Systems 20 Proceedings of the 2007 Conference
·
January 1, 2008
This article discusses a latent variable model for inference and prediction of symmetric relational data. The model, based on the idea of the eigenvalue decomposition, represents the relationship between two nodes as the weighted inner-product of node-spec ...
Cite
Conference
Methodology
·
January 1, 2006
Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently develope ...
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Conference
DYNAMIC SOCIAL NETWORK MODELING AND ANALYSIS
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January 1, 2003
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Conference
DYNAMIC SOCIAL NETWORK MODELING AND ANALYSIS
·
2003
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