Counterfactual learning for higher-order relation prediction in heterogeneous information networks.

Abstract

Heterogeneous Information Networks (HINs) play a crucial role in modeling complex social systems, where predicting missing links/relations is a significant task. Existing methods primarily focus on pairwise relations, but real-world scenarios often involve multi-entity interactions. For example, in academic collaboration networks, an interaction occurs between a paper, a conference, and multiple authors. These higher-order relations are prevalent but have been underexplored. Moreover, existing methods often neglect the causal relationship between the global graph structure and the state of relations, limiting their ability to capture the fundamental factors driving relation prediction. In this paper, we propose HINCHOR, an end-to-end model for higher-order relation prediction in HINs. HINCHOR introduces a higher-order structure encoder to capture multi-entity proximity information. Then, it focuses on a counterfactual question: "If the global graph structure were different, would the higher-order relation change?" By presenting a counterfactual data augmentation module, HINCHOR utilizes global structure information to generate counterfactual relations. Through counterfactual learning, HINCHOR estimates causal effects while predicting higher-order relations. The experimental results on four constructed benchmark datasets show that HINCHOR outperforms existing state-of-the-art methods.