Date & Time:
Thursday, May 2, 2013; 12:00 PM
Graph theory provides an intuitive mathematical foundation for dealing with relational data, but there are numerous computational challenges in the detection of interesting behavior within small subsets of vertices, especially as the graphs grow larger and the behavior becomes more subtle. This presentation discusses computational considerations of a residuals-based subgraph detection framework, including the implications on inference with recent statistical models. We also present scaling properties, demonstrating analysis of a billion-vertex graph using commodity hardware.