Difference between revisions of "Chang and Blei, AOAS2010"
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== Citation == | == Citation == | ||
J. Chang and D. Blei. Hierarchical relational models for document networks. Annals of Applied Statistics, 4(1):124–150, 2010 | J. Chang and D. Blei. Hierarchical relational models for document networks. Annals of Applied Statistics, 4(1):124–150, 2010 | ||
+ | |||
+ | == Online version == | ||
+ | [http://www.cs.princeton.edu/~blei/papers/ChangBlei2010.pdf D.Blei's papers] | ||
+ | |||
+ | == 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 <math>d</math>: | ||
+ | |||
+ | (a) Draw topic proportions <math>\theta_d|\alpha \approx Dir(\alpha)</math> | ||
+ | |||
+ | (b) For each word <math>w_{d,n}</math>: | ||
+ | |||
+ | i. Draw assignment <math>z_{d,n}|\theta_d \approx Mult(\theta_d)</math>. | ||
+ | |||
+ | ii. Draw word <math>w_{d,n}|z_{d,n},\beta_{1:K} \approx 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'} \approx \psi(.|z_d,z_{d'},\eta)</math> | ||
+ | |||
+ | where <math>z_d = \{z_{d,1},z_{d,2},...,z_{d,n}\}</math> |
Revision as of 12:10, 24 February 2011
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