|Workshop on Statistical Inference, Computing and Visualization for Graphs|
|August 1 - 2, 2003|
Links to : Contributed Talks --- Software Demos --- Printable Version of Program
Day 1- Friday, August 1
|Applications and Challenges|
|8:45-9:15||Transactional Data Overview||Daryl Pregibon|
|9:20-9:50||Social Networks Overview (slides)||Mark Handcock|
|10:30-11:00||Link Mining (slides.ppt)||Lise Getoor|
|11:00-11:30||Bioinformatics Overview (slides)||Susan Holmes|
|11:30-12:15||Group Discussion||led by Robert Gentleman|
|Software and Algorithms|
|1:30-2:00||Graph Drawing and Layout Overview (slides)||
|2:00-2:30||Graph Algorithms Overview (slides.ppt)||Sridhar Rajagopalan|
|3:00-3:20||Graph Drawing and Analysis in R (slides)||Vince Carey|
|3:20-3:40||GGobi (slides)||Debby Swayne|
|3:45-4:15||Group Discussion||led by Duncan Temple Lang|
Day 2 - Saturday, August 2
|Exploratory Data Analysis|
|8:45-9:15||Exploratory Data Analysis with Graphs (slides.ppt)||Chris Volinsky|
|9:15-9:45||Exploratory Data Analysis-Bioinformatics (slides)||Robert Gentleman|
Discussion and Break
|Statistical Inference and Modeling with Graph Data - Research Talks - (click for abstract)|
|10:30 - 12PM||
Deepak Agarwal - AT&T Labs "Statistical Inference for Large Directed Graphs with Communities of Interest" (slides)
Mark S. Handcock - University of Washington "Degeneracy and Inference for Social Networks Models"
Denise Scholtens - Harvard School of Public Health "Graph theory and statistical inference considerations for protein-protein interaction and gene expression data" (slides)
|1:30 - 3:00||Cliff Behrens -
Telcordia, Inc "Graphical Representations of Knowledge and Its Distribution"
Daniel A. McFarland - Stanford Universtiy "Dynamic Network Visualization: Methods for Meaning with Longitudinal Network Movies" (slides.ppt)
Scott White - University of California - Irvine "A Brief Guided Tour of the Java Universal Network/Graph Framework" (web page, slides)
|3:00||Wrapup Discussion - Andreas Buja|
Day 1, morning: Applications and challenges
Presentations on different application areas: What does the data look like? What approaches are being used, and what challenges does the data pose? These should be high level talks that introduce the field to the unfamiliar, and present the particular challenges faces with regard to using graphs for statistical analysis.
Day 1, afternoon: Software and Algorithms
Presentations on existing software that can be used for graphs, the strengths and limitations and challenges for the graph drawing comminity which are specific to statistical analysis. We will have representatives from the graph algorithms community as well as the statistical software community -- how can we bring these two together?
Graph Software - What tools are in use now? What's good about them, and where do they fall short?
Statistical software for Graphs - where do statisticians interface with the technologies above?
Day 2, morning and early afternoon: Statistical Inference and EDA for Graphs
Discussions of the various types of analysis we are doing with graphs, with specific examples. This section would give participants an opportunity to give a short talk on their specific research, with the hope that it ties in to previous sections.
Day 2, late afternoon: Summing up
One topic for this final session is a conversation between users and software designers: what requests do users have for the software designers, and what questions would the designers like to ask of the prospective users?