Workshop on

Analyzing Networks and Learning with Graphs

NIPS 2009

Recent research in machine learning and statistics has seen the proliferation of computational methods for analyzing networks and learning with graphs. These methods support progress in many application areas, including the social sciences, biology, medicine, neuroscience, physics, finance, and economics.

The primary goal of the workshop is to actively promote a concerted effort to address statistical, methodological and computational issues that arise when modeling and analyzing large collection of data that are largely represented as static and/or dynamic graphs. To this end, we aim at bringing together researchers from applied disciplines such as sociology, economics, medicine and biology, together with researchers from more theoretical disciplines such as mathematics and physics, within our community of statisticians and computer scientists. Different communities use diverse ideas and mathematical tools; our goal is to to foster cross-disciplinary collaborations and intellectual exchange.

Presentations will include novel graph models, the application of established models to new domains, theoretical and computational issues, limitations of current graph methods and directions for future research.Call for papers

- Matthew Jackson (Department of Economics, Stanford University)
- Ravi Kumar (Yahoo! Research)
- Martina Morris (Departments of Sociology and Statistics, University of Washington)
- Cosma Shalizi (Department of Statistics, CMU)
- Eric Xing (Machine Learning Department, CMU)

7:30-7:35 | Introduction by the organizers |

7:35-8:15 | Matthew Jackson: Identifying Choice and Chance in Network Formation |

8:15-8:55 | Poster spotlight presentations |

9:00-9:15 | Break and poster set-up |

9:15-9:55 | Eric Xing: Dynamic Network Tomography |

9:55-10:35 | Cosma Shalizi: Homophily, Contagion, Confounding: Pick Any Three |

10:35-3:30 | BREAK (POSTERS) |

3:30-4:15 | Martina Morris: Partnership Networks and HIV: Applications of Dynamic ERGMS to understand epidemic disparities |

4:15-5:00 | Ravi Kumar: Compressibility of Behavioral Graphs |

5:00-5:30 | BREAK (POSTERS) |

5:30-6:00 | Panel discussion and closing remarks |

On Doubly Stochastic Graph Optimization by A. Bijral, N. Srebro

Link-based Active Learning by M. Bilgic, L. Getoor

Fast and Optimal Algorithms for Weighted Graph Prediction by N. Cesa-Bianchi, C. Gentile, F. Vitale, G. Zappella

Chance-Constrained Programs for Link Prediction by J. Doppa, J. Yu, P. Tadepalli, L. Getoor

Multi-label Prediction for Learning in Relational Graphs by Y. Huang, V. Tresp

Path and travel time inference from GPS probe vehicle data by T. Hunter, R. Herring, P. Abbeel, A. Bayen

Biological Network Integration and Mining for Microbial Community Analysis by C. Huttenhower

From Sensor Network To Social Network - A Study On The Energy Impact In Buildings by X. Jiang, B. Dong, L. Sweeney

Continuous Time Group Discovery in Dynamic Graphs by K. Miller, T Eliassi-Rad

Learning latent structure in complex networks by M. Morup, l. Hansen

Towards Community Detection in k-Partite k-Uniform Hypergraphs by N. Neubauer

Inferring Interests from Mobility and Social Interactions by A. Noulas, M. Musolesi, M. Pontil, C. Mascolo

Confident Network Indices with Latent Space Models by J. Olson, K. Carley

Hierarchies in Dictionary Definition Space by O. Picard, A. Masse, S. Harnad, O. Marcotte, G. Chicoisne, Y. Gargouri

The Resistance Distance is Meaningless for Large Random Geometric Graphs by A. Radl, U. von Luxburg, M. Hein

RandomWalks with Random Projections by P. Sarkar

Collective Classification with Content and Link Noise by B. Senliol, Z. Cataltepe, A. Sonmez

Mixed-Membership Stochastic Block-Models for Transactional Data by M. Shafiei, H. Chipman

Marginally Specified Hierarchical Models for Relational Data by A. Thomas, J. Blitzstein

Variational Bayesian Inference for the Latent Position Cluster Model by M. Townsend, B. Murphy

Learning curves for Gaussian process regression on random graphs: effects of graph structure by M. Urry, P. Sollich

Learning the Dynamics and Strength of Face-to-Face Interaction Networks from Situated Speech Data by D. Wyatt, T. Choudhury, J. Bilmes

Modeling Relationship Strength in by R. Xiang, J. Neville, M. Rogati

Active Learning for Hidden Attributes in Networks by X. Ran Yan, Y. J. Zhu, J.B. Rouquier, C. Moore

Rumors in a Network: Who's the Culprit? by T. Zaman

Workshop is in **Westin** room **Nordic**.

The workshop will be held in conjunction with the 22nd Annual Conference on Neural Information Processing Systems,

Whistler, BC, Canada, December 11, 2009.

- Edoardo Airoldi, Harvard University
- Jon Kleinberg, Cornell University
- Jure Leskovec, Stanford University
- Josh Tenenbaum, MIT

You can reach the organizers at nipsgraphs2009@gmail.com