- 12.5.2012: Workshop held at NIPS 2012
A workshop on algorithmic and statistical approaches for large social network data sets took place on December 7, 2012 at Lake Tahoe, Nevada alongside NIPS. See the workshop website for find more information on the presenters.
- 4.2.2012: Sunbelt Conference
At the 32nd annual Sunbelt Conference (the flagship event for the International Network for Social Network Analysis), work from this MURI project played a prominent role. The conference began with a set of workshops, five of which were based on statnet software and methodological advances made by this MURI grant. This included new workshops on time-separable ERGM analysis and relational event modeling, both well-attended by dozens of people interested in applying the methods in their own research. The majority of students funded by the project gave 20 minute oral presentations. In addition, the impact of MURI relational event research was clear; two sessions of talks and a panel discussion were dedicated to relational event modeling extensions and applications.
For more information see the Sunbelt Conference website.
- 1.3.2012: Annual Review Meeting on January 10th, 2012
- 5.31.2011: Annual Review Meeting on June 3rd, 2011
Information regarding the June meeting is now posted here. The full schedule is available; PDFs of each presentation will be posted soon.
- 11.15.2010: Annual Review Meeting on November 12th, 2010
Information regarding the recent meeting is now posted here. The full schedule is available along with the PDFs of each presentation and poster.
- 11.1.2010 Major New Publication on Treatment of Missing Data in Networks
This new paper lays out a coherent framework for treatment of missing data in statistical models of social network data and is a major step forward in allowing practitioners to handle missing data in a principled manner:
Mark S. Handcock and Krista J. Gile "Modeling Networks from Sampled Data" (2010) Annals of Applied Statistics, 4, Number 1, 5-25. Corresponding software (R code) for handling missing data has also been released in the "ergm" package.
- 9.26.2010 Löffler and Nöllenberg win Graph Drawing Challenge....
About the Project
This MURI project is focused on developing new and innovative methods for analysis of large complex network data sets, focusing in particular on scalable algorithms for statistical analysis for social network data. Statistical modeling of social networks is well-established, but the widespread application of these ideas to network data sets has been limited to date by computational limitations. In particular, our ability to collect very large and more complex network data sets (e.g., via the Web) means that there is increasing demand for analysis and modeling algorithms that scale well with data size and complexity, and that can be used to provide insights, to test hypotheses, and to make predictions. This project is focused on developing new techniques and tools for addressing this scalability problem, including topics such as development of efficient data structures for modeling network data, fast Monte Carlo sampling algorithms for network parameter estimation, new latent variable models for analyzing networks over time and networks with text data, systematic methods for handling missing data in networks, among others. Data sets being used in the project include interorganizational communication data for disaster recovery (Katrina and World Trade Center), email communication data over time, Twitter data, political blog data, as well as many more traditional social network data sets. The project personnel consists of an interdisciplinary team with expertise spanning sociology, statistics, machine learning, algorithms, and computational geometry, involving 7 professors, 3 postdoctoral researchers, and about 15 PhD students, distributed across 5 universities.