Overview

Networks are used across a wide variety of disciplines to describe interactions between entities - in sociology these are relations between people, such as friendships (Facebook); in biology -- physical interactions between genes; the Internet, sensor networks, transport networks, ecological networks just to name a few. The applications of networks provide a wide variety of questions while researchers in machine learning, statistical and physics communities search for ways to explain and model the observed phenomena. The theoretical findings stemming from different areas are heterogeneous and often complimentary yet there are but a few means for intellectual exchange and collaboration.

The goal of our workshop is to actively promote a collaborative effort in addressing statistical and computational issues arising when modeling collections of data represented by networks -- static or dynamic; to "cross-pollinate" fields with ideas from different areas; to introduce new questions to the theoretical modeling audience and to broaden the focus by considering new areas.

Carter Butts  (Sociology, UC Irvine)

Jonathan Chang  (Facebook)

Aaron Clauset  (Computer Science, University of Colorado Boulder)

Lise Getoor  (Computer Science, University of Maryland)

Sayan Mukherjee  (Statistics, Duke University)

7:30-7:35        Introduction by the organizers

7:35-8:20        Lise Getoor: Collective Graph Identification

8:20-9:05        Aaron Clauset: The Trouble with Community Detection

9:05-9:30        Break and poster set-up

9:30-10:00      Poster Spotlights

10:00-12:00    POSTERS

12:00-15:30    Break and ski

15:30-16:15    Carter Butts: Bounding Complex Network Models with Bernoulli Graphs

16:15-17:00    Sayan Mukherjee: Geometry based graph and network models

17:00-17:15    coffee break

17:15-18:00    Jonathan Chang: Facebook: Challenges for 2011

18:00-18:30    Panel discussion and closing remarks

Accepted posters

1. 1.Active Surveying, H. Sharara, L. Getoor, M. Norton (pdf)
   

2. 2.Active Learning on Graphs via Spanning Trees, N. Cesa-Bianchi, C. Gentile,F. Vitale, G. Zappella (pdf)
   

3. 3.Unsupervised discriminative approach to find biomarkers in lung cancer, A. Goldenberg, S. Mostafavi, G. Quon, P. C. Boutros, Q. Morris
   

4. 4.Predicting Node Labels in Large Networks, S. Mostafavi, A. Goldenberg, Q. Morris
   

5. 5.Beyond Keyword Search: Discovering Relevant Scientific Literature, K. El-Arini, C. Guestrin
   

6. 6.Statistical Mechanics of Semi-Supervised Clustering in Sparse Graphs, A. Galstyan, G. Ver Steeg, A. Allahverdyan
   

7. 7.Co-Evolving Mixed Membership BlockModels, Y. Cho, A. Galstyan, G. Ver Steeg  (pdf)
   

8. 8.Infinite Multiple Membership Relational Modeling for Complex Networks, M. Mørup, M. N. Schmidt, L. K. Hansen (pdf)
   

9. 9.Soft Partitioning in Networks via Bayesian Non-negative Matrix Factorization, I. Psorakis, S.Roberts, B. Sheldon (pdf)
   

10. 10.Estimating Networks with Jumps, M. Kolar, E. P. Xing (pdf)
    

11. 11.Community Detection in Networks: The Leader-Follower Algorithm, D. Shah, T. R. Zaman (pdf)
    

12. 12.Node Clustering in Graphs: An Empirical Study, R. Balasubramanyan, F. Lin, W. W. Cohen (pdf)
    

13. 13.Community Finding: Partitioning Considered Harmful, F. Reid, A. McDaid, N. Hurley (pdf)
    

14. 14.A Latent Space Mapping for Link Prediction, A. Brew, M. Salter-Townshend (pdf)
    

15. 15.Regularized Output Kernel Regression applied to protein-protein interaction network inference, Celine.Brouard, M. Szafranski, F. d'Alché-Buc (pdf)
    

16. 16.RegnANN: network inference using Artificial Neural Networks, M. Grimaldi, G. Jurman, R. Visintainer  (pdf)
    

17. 17.Introduction to spectral metrics in biological network theory, R. Visintainer, G. Jurman, M. Grimaldi, C. Furlanello  (pdf)
    

18. 18.A Triangle Inequality for p-Resistance, M.Herbster (pdf)
    

19. 19.Sampling Graphs with a Prescribed Joint Degree Distribution Using Markov Chains, I. Stanton, A. Pinar
    

20. 20.Population size estimation and Internet link structure, S. E. Fienberg, A. Flaxman  (pdf)
    

21. 21.Modeling the Variance of Network Populations with Mixed Kronecker Product Graph Models, S. Moreno, J. Neville, S. Kirshner, S.V.N. Vishwanathan  (pdf)
    

22. 22.Exact learning curves for Gaussian process regression on community random graphs, M. J. Urry, P. Sollich  (pdf)
    

23. 23.Higher-order Graphical Models for Classification in Social and Affiliation Networks, E. Zheleva, L. Getoor, S. Sarawagi (pdf)
    

24. 24.Four factors influencing effectiveness in email communication networks, O. Engel (pdf)