gRegress: Extracting Features from Graph Transactions for Regression

Nikhil S. Ketkar, Lawrence B. Holder, Diane J. Cook

Abstract:

In this work we propose gRegress, a new algorithm which given set a of labeled graphs and a real value associated with each graph extracts the complete set of subgraphs such that a) each subgraph in this set has correlation with the real value above a user-specified threshold and b) each subgraph in this set has correlation with any other subgraph in the set below a user-specified threshold. gRegress incorporates novel pruning mechanisms based on correlation of a subgraph feature with the output and correlation with other subgraph features. These pruning mechanisms lead to significant speedup.

Experimental results indicate that in terms of run-time, gRegress substantially outperforms gSpan, often by an order of magnitude while the regression models produced by both approaches have comparable accuracy.