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Bayesian nonparametric models for bipartite graphs
Published on Jan 16, 20133561 Views
We develop a novel Bayesian nonparametric model for random bipartite graphs. The model is based on the theory of completely random measures and is able to handle a potentially infinite number of nodes
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Chapter list
Bayesian Nonparametric Models for Bipartite Graphs00:00
Bipartite networks (1)00:01
Bipartite networks (2)00:34
Bipartite networks (3)00:46
Bipartite networks (4)01:08
Bipartite networks (5)01:41
Bipartite networks (6)02:11
Bipartite networks (7)02:23
Bipartite networks (8)02:59
Hierarchical model (1)03:47
Hierarchical model (2)04:18
Hierarchical model (3)04:22
Hierarchical model (4)04:29
Data Augmentation04:51
Model for the book popularity parameters06:20
Predictive distribution07:42
Generative Process for network growth (1)09:01
Generative Process for network growth (2)09:16
Generative Process for network growth (3)09:26
Generative Process for network growth (4)09:32
Generative Process for network growth (5)09:35
Generative Process for network growth (6)09:46
Generative Process for network growth (7)09:52
Generative Process for network growth (8)09:59
Generative Process for network growth (9)10:05
Generative Process for network growth (10)10:07
Prior Draws10:15
Bayesian Inference via Gibbs Sampling11:28
Model for the \interest in reading" parameters13:18
Application14:13
Application: IMDB Movie Actor network 15:16
Application: Book-crossing community network16:16
Summary (1)16:38
Summary (2)17:06
Bibliography17:31