Item

Abstract

Attributed graphs are increasingly more common in many application
domains such as chemistry, biology and text processing.
A central issue in graph mining is how to collect informative subgraph
patterns for a given learning task.
We propose an iterative mining method based on partial least squares regression (PLS). To apply PLS to graph data, a sparse version of PLS is developed first and then it is combined with a weighted pattern mining algorithm.
The mining algorithm is iteratively called with different weight
vectors, creating one latent component per one mining call.
Our method, graph PLS, is efficient and easy to implement, because the
weight vector is updated with elementary matrix calculations.
In experiments, our graph PLS algorithm showed
competitive prediction accuracies in many chemical datasets and its
efficiency was significantly superior to graph boosting (gboost) and the
naive method based on frequent graph mining.