Difference between revisions of "Class Meeting for 10-802 02/17/2011"
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=== Overview === | === Overview === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-802/02-17- | + | * Rest of the [http://www.cs.cmu.edu/~wcohen/10-802/02-15-spectral-catchup.pptx slides] from 2-15. |
| + | * [http://www.cs.cmu.edu/~wcohen/10-802/02-17-prob-graphs.pptx Slides] for today's lecture. | ||
=== Readings === | === Readings === | ||
| − | * | + | * [http://dash.harvard.edu/bitstream/handle/1/4645865/Goldenberg_NetSurvey.pdf?sequence=1 A survey of statistical network models], Goldenberg, Zheng, Fienberg, and Airoldi, Sections 1-3. (If you like, you can glance over Section 4.1 - 4.3, which give this author's view of some of the other models we've discussed.) |
| + | * I will also discuss some of the inference issues for estimating parameters of probabilistic graph models, and will discuss this short paper: Juuso Parkkinen, Adam Gyenge, Janne Sinkkonen and Samuel Kaski. [http://www.cis.hut.fi/projects/mi/papers/mlg09sibm.pdf A block model suitable for sparse graphs.] In MLG 2009, The 7th International Workshop on Mining and Learning with Graphs, Leuven, Belgium, July 2-4,2009. | ||
| + | |||
| + | === Optional Readings === | ||
| + | |||
| + | I use these two papers as additional background: | ||
| + | |||
| + | * Snijders, T.A.B. & Nowicki, K., Estimation and prediction for stochastic block models for graphs with latent block structure. Journal of Classification, 14 (1997), 75 - 100. | ||
| + | * MS Handcock, AE Raftery, JM Tantrum, Model-based clustering for social networks, Journal of the Royal Stat Soc Series A, 2007, 170(2), pages 301-354 | ||
| + | |||
| + | Some recent papers that could be presented at this point: | ||
| + | * Leskovec, J., D. Huttenlocher, and J. Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proceedings of the 19th international conference on World wide web, 641–650. | ||
| + | * Leskovec, J., K. J Lang, and M. Mahoney. 2010. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th international conference on World wide web, 631–640. | ||
Latest revision as of 15:09, 14 March 2011
This is one of the class meetings on the schedule for the course Social Media Analysis 10-802 in Spring 2011.
Overview
Readings
- A survey of statistical network models, Goldenberg, Zheng, Fienberg, and Airoldi, Sections 1-3. (If you like, you can glance over Section 4.1 - 4.3, which give this author's view of some of the other models we've discussed.)
- I will also discuss some of the inference issues for estimating parameters of probabilistic graph models, and will discuss this short paper: Juuso Parkkinen, Adam Gyenge, Janne Sinkkonen and Samuel Kaski. A block model suitable for sparse graphs. In MLG 2009, The 7th International Workshop on Mining and Learning with Graphs, Leuven, Belgium, July 2-4,2009.
Optional Readings
I use these two papers as additional background:
- Snijders, T.A.B. & Nowicki, K., Estimation and prediction for stochastic block models for graphs with latent block structure. Journal of Classification, 14 (1997), 75 - 100.
- MS Handcock, AE Raftery, JM Tantrum, Model-based clustering for social networks, Journal of the Royal Stat Soc Series A, 2007, 170(2), pages 301-354
Some recent papers that could be presented at this point:
- Leskovec, J., D. Huttenlocher, and J. Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proceedings of the 19th international conference on World wide web, 641–650.
- Leskovec, J., K. J Lang, and M. Mahoney. 2010. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th international conference on World wide web, 631–640.
