Difference between revisions of "Mrinmaya et. al. WWW'12"

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== Summary ==
 
== Summary ==
−
In this paper, the authors study the problem of communities detection in social networks. They employ the probabilistic approach and propose a generative model that describes how users' messages and interactions are generated from the hidden membership of each user.
+
In this paper, the authors study the problem of communities detection in social networks. They employ the probabilistic approach and propose a generative model that describes how users' messages and interactions are generated from the hidden membership of each user. The general model, or ``full model" as called by the author, has the generative process as follows.
 +
*For each of the topics, 1 � z � Z, sample a V dimensional multinomial,
 +
<math>~�z � DirV (�\theta) </math>
 +
2. For each of the communities, 1 � c � C sample a
 +
X dimensional social type interaction mixture �~
 +
c �
 +
DirX(�).
 +
3. For each of the communities, 1 � c � C sample a U
 +
dimensional social recipient interaction mixture  ~
 +
c �
 +
DirU (�).
 +
4. For the i
 +
th
 +
user ui, 1 � ui � U:
 +
(a) Sample a C dimensional multinomial,
 +
~
 +
�ui � DirC(�),
 +
representing the community proportions for that
 +
sender.
 +
(b) For each community c 2 C, sample a Z dimen-
 +
sional multinomial, ~�ui;c � DirZ(�), representing
 +
the topic proportions for community and sender.
 +
(c) For each post p (1 � p � Pi) generated by the
 +
sender ui: having Np words:
 +
i. Choose a community assignment cp � Mult(
 +
~
 +
�ui
 +
)
 +
cp 2 [1 : C] for the post.
 +
ii. For each recipient slot i, 1 � i � Rp of the
 +
post p:
 +
A. Choose a recipient rp � Mult( ~
 +
cp
 +
)
 +
rpi 2 [1 : Rp] for the post.
  
 
== Dicussion ==
 
== Dicussion ==

Revision as of 11:15, 2 October 2012

This is a scientific paper authored by Mrinmaya Sachan, and appeared in WWW'12. Below is the paper summary written by Tuan Anh.

Citation

@inproceedings{Sachan:2012:UCI:2187836.2187882, 

author = {Sachan, Mrinmaya and Contractor, Danish and Faruquie, Tanveer A. and Subramaniam, L. Venkata},
title = {Using content and interactions for discovering communities in social networks},
booktitle = {Proceedings of the 21st international conference on World Wide Web},
series = {WWW '12},
year = {2012},
isbn = {978-1-4503-1229-5},
location = {Lyon, France},
pages = {331--340},
numpages = {10},
url = {http://doi.acm.org/10.1145/2187836.2187882},
doi = {10.1145/2187836.2187882},
acmid = {2187882},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {community detection, probabilistic methods, social networks},

}

Online Version

Using Content and Interactions for Discovering Communities in Social Networks.

Summary

In this paper, the authors study the problem of communities detection in social networks. They employ the probabilistic approach and propose a generative model that describes how users' messages and interactions are generated from the hidden membership of each user. The general model, or ``full model" as called by the author, has the generative process as follows.

  • For each of the topics, 1 � z � Z, sample a V dimensional multinomial,
Failed to parse (syntax error): {\displaystyle ~�z � DirV (�\theta) }

2. For each of the communities, 1 � c � C sample a X dimensional social type interaction mixture �~ c � DirX(�). 3. For each of the communities, 1 � c � C sample a U dimensional social recipient interaction mixture ~ c � DirU (�). 4. For the i th user ui, 1 � ui � U: (a) Sample a C dimensional multinomial, ~ �ui � DirC(�), representing the community proportions for that sender. (b) For each community c 2 C, sample a Z dimen- sional multinomial, ~�ui;c � DirZ(�), representing the topic proportions for community and sender. (c) For each post p (1 � p � Pi) generated by the sender ui: having Np words: i. Choose a community assignment cp � Mult( ~ �ui ) cp 2 [1 : C] for the post. ii. For each recipient slot i, 1 � i � Rp of the post p: A. Choose a recipient rp � Mult( ~ cp ) rpi 2 [1 : Rp] for the post.

Dicussion

Related papers

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