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.

中文翻译：

---

高效的自监督异构图表示学习与重建


异构图表示学习 （HGRL） 作为处理异构图数据的强大技术之一，已显示出卓越的性能并引起了越来越多的关注。然而，现有的 HGRL 方法仍然面临有待解决的问题：（i） 它们捕获了不同基于元路径的视图之间的一致性，从而引发了昂贵的计算成本，并可能导致维度崩溃。（ii） 他们忽略了每个基于元路径的观点中的互补性，从而降低了模型的有效性。为了缓解这些问题，在本文中，我们提出了一种新的自我监督 HGRL 框架来捕获不同视图之间的一致性，保持每个视图内的互补性，并避免维度崩溃。具体来说，所提出的方法研究了相关性损失，以捕捉不同视图之间的一致性并减少维度冗余，并研究了重建损失，以保持每个视图内的互补性，从而有利于下游任务。我们进一步从理论上证明，所提出的方法可以有效地将任务相关信息整合到节点表示中，从而提高下游任务的性能。在多个公共数据集上进行的广泛实验验证了所提方法对下游任务的有效性和效率。