Knowledge Graph Constraints for Multi-label Graph Classification

Graph classification methods have gained increasing attention in different domains, such as classifying functions of molecules or detection of bugs in software programs. Similarly, predicting events in manufacturing operations data can be compactly modeled as graph classification problem. Feature representations of graphs are usually found by mining discriminative sub-graph patterns that are non-uniformly distributed across class labels. However, as these feature selection approaches are computationally expensive for multiple labels, prior knowledge about label correlations should be exploited as much as possible. In this work, we introduce a new approach for mining discriminative sub-graph patterns with constraints that are extracted from links between labels in knowledge graphs which indicate label correlations. The incorporation of these constraints allows to prune the search space and ensures extraction of consistent patterns. Therefore, constraint checking remains efficient and more robust classification results can be obtained. We evaluate our approach on both, one public and one custom simulated data set. Evaluation confirms that incorporation of constraints still results in efficient pattern mining and can increase performance of state-of-the-art approaches.