Graph classification aims to learn models to classify structure data. To date, all existing graph classification methods are designed to target one single learning task and require a large number of labelled samples for learning good classification models. In reality, each real-world task may only have a limited number of labelled samples, yet multiple similar learning tasks can provide useful knowledge to benefit all tasks as a whole. In this paper, we formulate a new multi-task graph classification (MTG) problem, where multiple graph classification tasks are jointly regularized to find discriminative sub graphs shared by all tasks for learning. The niche of MTG stems from the fact that with a limited number of training samples, sub graph features selected for one single graph classification task tend to over fit the training data. By using additional tasks as evaluation sets, MTG can jointly regularize multiple tasks to explore high quality sub graph features for graph classification. To achieve this goal, we formulate an objective function which combines multiple graph classification tasks to evaluate the informativeness score of a sub graph feature. An iterative sub graph feature exploration and multi-task learning process is further proposed to incrementally select sub graph features for graph classification. Experiments on realworld multi-task graph classification datasets demonstrate significant performance gain.
As for the task classfication we conducted, REF formulated an objective function which combined multiple graph classification tasks to evaluate the informativeness score of a subgraph feature.