Parallel Graph Mining with GPUs
We develop parallel versions of graph mining algorithms that achieve significant improvement in runtime. The approach exploits GPU parallelism to accelerate frequent subgraph …
Approximate Graph Mining with Label Costs
We present novel and scalable methods for approximate frequent subgraph mining from exact and probabilistic graphs. By incorporating label costs, the approach yields more …
Communities and Balance in Signed Networks: A Spectral Approach
We develop a spectral approach for detecting communities in signed networks, where edges carry positive and negative weights. The method leverages structural balance theory to find …
Infrastructure Pattern Discovery in Configuration Management Databases via Large Sparse Graph Mining
We apply large sparse graph mining to discover infrastructure patterns in configuration management databases (CMDBs). The method identifies recurring structural patterns in IT …