Chang and Blei, AOAS2010
J. Chang and D. Blei. Hierarchical relational models for document networks. Annals of Applied Statistics, 4(1):124–150, 2010
For Network data, such as social networks of friends, citation networks of documents or hyperlinked networks of web pages, people want to point social network members toward new friends, scientific papers toward relevant citations or web pages toward other related pages. They also want to uncover the hidden community structure. This paper developed a hierarchical model of both network structure and node attributes, based on Latent Dirichlet Allocation.
1. For each document :
(a) Draw topic proportions (b) For each word : i. Draw assignment . ii. Draw word .
2. For each pair of documents ,:
(a) Draw binary link indicator where
Inference, Estimation and Prediction
- Link prediction from words
- Words prediction from link
- Cora: abstracts + citation link
- WebKB: web pages + hyperlinks
- PNAS: abstracts + intra-PNAS citation
- LocalNews: local news of each state in U.S + geographical adjacency
- Evaluating the predictive distribution
- Automatic link suggestion
- Modeling spatial data
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Blei et al, NIPS2007 BLEI, D. M. and MCAULIFFE, J. D. (2007). Supervised topic models. In Neural Information Processsing Systems. Vancouver.
Nallapati et al, ACM2008 NALLAPATI, R., AHMED, A., XING, E. P. and COHEN, W. W. (2008). Joint latent topic models for text and citations. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 542–550. ACM Press, New York.