Multi-label Feature Selection for Graph Classification

Nowadays, the classification of graph data has become an important and active research topic in the last decade, which has a wide variety of real world applications, e.g. drug activity predictions and kinase inhibitor discovery. Current research on graph classification focuses on single-label settings. However, in many applications, each graph data can be assigned with a set of multiple labels simultaneously. Extracting good features using multiple labels of the graphs becomes an important step before graph classification. In this paper, we study the problem of multi-label feature selection for graph classification and propose a novel solution, called gMLC, to efficiently search for optimal sub graph features for graph objects with multiple labels. Different from existing feature selection methods in vector spaces which assume the feature set is given, we perform multi-label feature selection for graph data in a progressive way together with the sub graph feature mining process. We derive an evaluation criterion, named gHSIC, to estimate the dependence between sub graph features and multiple labels of graphs. Then a branch-and-bound algorithm is proposed to efficiently search for optimal sub graph features by judiciously pruning the sub graph search space using multiple labels. Empirical studies on real-world tasks demonstrate that our feature selection approach can effectively boost multi-label graph classification performances and is more efficient by pruning the sub graph search space using multiple labels.