Efficient self-supervised heterogeneous graph representation learning with reconstruction

Heterogeneous graph representation learning (HGRL), as one of powerful techniques to process the heterogeneous graph data, has shown superior performance and attracted increasing attention. However, existing HGRL methods still face issues to be addressed: (i) They capture the consistency among different meta-path-based views to induce expensive computation costs and possibly cause dimension collapse. (ii) They ignore the complementarity within each meta-path-based view to degrade the model’s effectiveness. To alleviate these issues, in this paper, we propose a new self-supervised HGRL framework to capture the consistency among different views, maintain the complementarity within each view, and avoid dimension collapse. Specifically, the proposed method investigates the correlation loss to capture the consistency among different views and reduce the dimension redundancy, as well as investigates the reconstruction loss to maintain complementarity within each view to benefit downstream tasks. We further theoretically prove that the proposed method can effectively incorporate task-relevant information into node representations, thereby enhancing performance in downstream tasks. Extensive experiments on multiple public datasets validate the effectiveness and efficiency of the proposed method on downstream tasks.