CODE: Towards Partial Label Graph Learning via Coupled Dual Separation

Graph classification is a fundamental machine learning problem with extensive applications in multimedia and biochemical analysis. Contemporary graph classification models usually require precise graph labels for supervision, even after self-supervised pre-training. However, in practical applications, the extensive precise annotation of graphs could be expensive or impractical. To exploit data efficiently, this work studies partial label graph learning, in which each graph is linked to a set of candidate labels but only one of them is accurate. Label ambiguity would bring difficulties in extracting graph semantics and the risk of overfitting noisy partial labels. Here, we present a novel approach called Coupled Dual Separation (CODE). To improve graph semantics mining under label ambiguity, our CODE contains a message passing branch and a graph kernel branch, which explore graph semantics implicitly and explicitly, respectively. To facilitate information exchange, we utilize one branch to separate partially labeled graphs into an informative set and an uninformative set, which provides guidance for the optimization of the other branch. Furthermore, to mitigate the risk of overfitting, parameters in coupled branches are partitioned into critical and non-critical ones for separated optimization procedures. Extensive experiments on several benchmark datasets validate the effectiveness of the proposed CODE.