A Multi-Task Representation Learning Architecture for Enhanced Graph Classification

Composed of nodes and edges, graph structured data are organized in the non-Euclidean geometric space and ubiquitous especially in chemical compounds, proteins, etc. They usually contain rich structure information, and how to effectively extract inherent features of them is of great significance on the determination of function or traits in medicine and biology. Recently, there is a growing interest in learning graph-level representations for graph classification. Existing graph classification strategies based on graph neural networks broadly follow a single-task learning framework and manage to learn graph-level representations through aggregating node-level representations. However, they lack the efficient utilization of labels of nodes in a graph. In this paper, we propose a novel multi-task representation learning architecture coupled with the task of supervised node classification for enhanced graph classification. Specifically, the node classification task enforces node-level representations to take full advantage of node labels available in the graph and the graph classification task allows for learning graph-level representations in an end-to-end manner. Experimental results on multiple benchmark datasets demonstrate that the proposed architecture performs significantly better than various single-task graph neural network methods for graph classification.