"Analyzing Networks and Learning with Graphs"

See you in Whistler?

Analyzing Networks and Learning with Graphs

a workshop in conjunction with

23nd Annual Conference on Neural Information Processing Systems (NIPS 2009)

December 11 or 12, 2009 (exact date TBD) Whistler, BC, Canada

Deadline for Submissions: Friday, October 30, 2009
Notification of Decision: Monday, November 9, 2009

Overview:

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.

Online Submissions

We welcome the following types of papers:

1. Research papers that introduce new models or apply established models to novel domains,
2. Research papers that explore theoretical and computational issues, or
3. Position papers that discuss shortcomings and desiderata of current approaches, or propose new directions for future research.

All submissions will be peer-reviewed; exceptional work will be considered for oral presentation. We encourage authors to emphasize the role of learning and its relevance to the application domains at hand. In addition, we hope to identify current successes in the area, and will therefore consider papers that apply previously proposed models to novel domains and data sets.

Submissions should be 4-to-8 pages long, and adhere to NIPS format. Please email your submissions to: nipsgraphs2009 [at] gmail [dot] com

Workshop Format

This is a one-day workshop. The program will feature invited talks, poster sessions, poster spotlights, and a panel discussion. All submissions will be peer-reviewed; exceptional work will be considered for oral presentation. More details about the program will be announced soon.

Organizers

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

Networks; Enigmas of Chance; Incestuous Amplification