Difference between revisions of "Chang and Blei, AOAS2010"
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== Citation == | == Citation == | ||
− | J. Chang and D. Blei. | + | J. Chang and D. Blei. Hierarchical relational models for document networks. Annals of Applied Statistics, 4(1):124–150, 2010 |
== Online version == | == Online version == | ||
Line 10: | Line 10: | ||
- citation networks of documents | - citation networks of documents | ||
- hyperlinked networks of web pages | - hyperlinked networks of web pages | ||
+ | |||
* “Predictive Models” | * “Predictive Models” | ||
- point social network members toward new friends | - point social network members toward new friends | ||
- point scientific papers toward relevant citations | - point scientific papers toward relevant citations | ||
- point web pages toward other related pages | - point web pages toward other related pages | ||
+ | |||
* “Descriptive statistics” | * “Descriptive statistics” | ||
- uncover the hidden community structure | - uncover the hidden community structure | ||
Line 20: | Line 22: | ||
1. For each document <math>d</math>: | 1. For each document <math>d</math>: | ||
− | (a) Draw topic proportions <math>\theta_d|\alpha \ | + | (a) Draw topic proportions <math>\theta_d|\alpha \sim Dir(\alpha)</math> |
− | |||
(b) For each word <math>w_{d,n}</math>: | (b) For each word <math>w_{d,n}</math>: | ||
+ | i. Draw assignment <math>z_{d,n}|\theta_d \sim Mult(\theta_d)</math>. | ||
+ | ii. Draw word <math>w_{d,n}|z_{d,n},\beta_{1:K} \sim Mult(\beta_{z_{d,n}})</math>. | ||
− | + | 2. For each pair of documents <math>d</math>,<math>d'</math>: | |
− | + | (a) Draw binary link indicator | |
+ | <math>y_{d,d'}|z_d,z_{d'} \sim \psi(.|z_d,z_{d'},\eta)</math> | ||
+ | where <math>z_d = \{z_{d,1},z_{d,2},...,z_{d,n}\}</math> | ||
− | + | == Inference, Estimation and Prediction == | |
+ | * Inference | ||
+ | * Estimation | ||
+ | * Prediction | ||
+ | - Link prediction from words | ||
+ | <math>p(y_{d,d'}|w_d, w_{d'}) \approx E_q [p(y_{d,d'}|\bar{z_d},\bar{z_{d'}})]</math> | ||
+ | - Words prediction from link | ||
+ | <math>p(w_{d,i}|y_d) \approx E_q [p(w_{d,i}|z_{d,i})]</math> | ||
− | + | == Data == | |
− | + | * Cora: abstracts + citation link | |
− | + | * WebKB: web pages + hyperlinks | |
+ | * PNAS: abstracts + intra-PNAS citation | ||
+ | * LocalNews: local news of each state in U.S + geographical adjacency | ||
− | + | == Results == |
Revision as of 12:23, 24 February 2011
Contents
Citation
J. Chang and D. Blei. Hierarchical relational models for document networks. Annals of Applied Statistics, 4(1):124–150, 2010
Online version
Motivation
- Network data
- social networks of friends - citation networks of documents - hyperlinked networks of web pages
- “Predictive Models”
- point social network members toward new friends - point scientific papers toward relevant citations - point web pages toward other related pages
- “Descriptive statistics”
- uncover the hidden community structure
Methodology
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
- Inference
- Estimation
- Prediction
- Link prediction from words - Words prediction from link
Data
- Cora: abstracts + citation link
- WebKB: web pages + hyperlinks
- PNAS: abstracts + intra-PNAS citation
- LocalNews: local news of each state in U.S + geographical adjacency