Schedule
- 07.30-07.35 Opening remarks
- 07.35-08.15 Invited talk: Graph-based methods for open information extraction, William Cohen (Carnegie Mellon University)
- 08.15-08.30 Connections between the lines: Extracting social networks from text, Jonathan Chang, Jordan Boyd-Graber, David Blei
- 08.30-08.45 Gibbs sampling for logistic normal topic models with graph based priors, David Mimno, Hanna M. Wallach, Andrew McCallum
- 08.45-08.55 Coffee Break
- 08.55-09.35 Invited talk: From here to eternity: Developing dynamic network models, Stephen Fienberg (Carnegie Mellon University)
- 09.35-10.30 Poster Session
- 10.30-03.00 Skiing / poster session cont'd
- 03.30-04.10 Invited talk: A statistical perspective on large-scale network data: The blending of inference and algorithms for analysis, Patrick Wolfe (Harvard University)
- 04.10-04.25 Maximum likelihood graph structure estimation with degree distributions, Bert Huang, Tony Jebara
- 04.25-04.40 Probabilistic graph models for debugging software, Laura Dietz, Valentin Dallmeier
- 04.40-04.50 Coffee Break
- 04.50-05.30 Invited talk: Size matters: Benefits from studying large networks, Jure Leskovec (Cornell University)
- 05.30-05.45 Time Varying Ising Models, Mladen Kolar, Eric Xing
- 05.45-06.00 Uncovering latent structure in valued graphs: A variational approach, Mahendra Mariadassou, Stephane Robin, Corrine Vacher
- 06.00-06.30 Panel Discussion
Accepted posters
- A path following algorithm for the graph matching problem Mikhail Zaslaviskiy, Francis Bach, Jean-Philippe Vert
- A simple infinite topic mixture for rich graphs and relational data Janne Sinkkonen, Juuso Parkkinen, Janne Aukia, Samuel Kaski
- Adjusting for Network Size and Composition Effects in Exponential Random Graphs Pavel Krivitsky
- Connections between the Lines: Extracting Social Networks from Text Jonathan Chang, Jordan Boyd-Graber, David Blei
- Gaussian Process Models for Colored Graphs Zhao Xu, Kristian Kersting, Volker Tresp
- Gibbs sampling for logistic normal topic models with graph based priors David Mimno, Hanna M. Wallach, Andrew McCallum
- Improved algorithm and data structures for modularity analysis of large networks Alexandre P. Francisco
- Large-scale Stochastic Relational Models Kai Yu, Shenghuo Zhu
- Maximum Likelihood Graph Structure Estimation with Degree Distributions Bert Huang, Tony Jebara
- Predicting gene function in a hierarchy Sara Mostafavi and Quaid Morris
- Probabilistic Graph Models for Debugging Software Laura Dietz, Valentin Dallmeier
- Re-weighting Graph Links for Quantifying Difference Yu-Shi Lin, Chung-Chi Lin, Yuh-Show Tsai, Tien-Chuan Ku, Yi-Hung Huang, Chun-Nan Hsu
- Selection of Regularization Parameter in Sparse MRF Learning: a Bayesian Approach Narges Bani Asadi, Irina Rish, Katya Scheinberg
- Sparse multiscale regression for graphical models Justin Guinney, Simon Lunagomez, Mauro Maggioni, Sayan Mukherjee
- Temporally-Evolving Mixed Membership Stochastic Blockmodels: Exploring the Enron E-mail Database Seungil Huh, Stephen E. Fienberg
- The Information in One Prior Relative to Another Michael Evans, Gun Ho Jang
- Time Varying Ising Models Mladen Kolar, Eric Xing
- Topic Models for Hypertext: How Many words is a single link worth? Amit Gruber, Michal Rosen-Zvi, Yair Weiss
- Uncovering Latent Structure in Valued Graphs: A Variational Approach Mahendra Mariadassou, Stephane Robin, Corrine Vacher
- Visualizing Graphs with Structure Preserving Embedding Blake Shaw, Tony Jebara
- Community detection: model fitting, comparison, and utility, Jake Hofman, Chris Wiggins, Duncan Watts
- The exchangeable graph model Edo Airoldi
For abstracts, please see the 2008 NIPS Workshop Program.