Abstract Graph contrastive learning has been developed to learn discriminative node representations on homogeneous graphs. However. it is not clear how to augment the heterogeneous graphs without substantially altering the underlying semantics or how to design appropriate pretext tasks to fully capture the rich semantics preserved in heterogeneous information networks (HINs). Moreover. https://www.roneverhart.com/William-Tuttle-Hair-White-Color-Foundation-6g/