document: Partial least squares regression for graph mining 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 .
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