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. | ||
| + | |||
| + | == Dicussion == | ||
| + | |||
| + | == Related papers == | ||
| + | *Airoldi. et. al. [http://jmlr.csail.mit.edu/papers/volume9/airoldi08a/airoldi08a.pdf Mixed Membership Stochastic Blockmodels]. Journal of Machine Learning Research 9 (2008) 1981-2014 | ||
Revision as of 10:19, 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.
Dicussion
Related papers
- Airoldi. et. al. Mixed Membership Stochastic Blockmodels. Journal of Machine Learning Research 9 (2008) 1981-2014
