Bayesian Hierarchical Community Discovery
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About this item
| Description: |
Teh, Y W (University of Oxford, University of Oxford)
Tuesday 26th July 2016 - 14:00 to 14:30 |
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| Created: | 2016-07-28 15:17 |
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| Collection: | Theoretical Foundations for Statistical Network Analysis |
| Publisher: | Isaac Newton Institute |
| Copyright: | Teh, Y W |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-author: Charles Blundell (Google DeepMind)
We propose an efficient Bayesian nonparametric model for discovering hierarchical community structure in social networks. Our model is a tree-structured mixture of potentially exponentially many stochastic blockmodels. We describe a family of greedy agglomerative model selection algorithms whose worst case scales quadratically in the number of vertices of the network, but independent of the number of communities. Our algorithms are two orders of magnitude faster than the infinite relational model, achieving comparable or better accuracy. Related Links http://papers.nips.cc/paper/5048-bayesian-hierarchical-community-discovery - NIPS paper page |
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