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

Area of Science:

• Computer Science
• Data Mining
• Network Science

Background:

• Heterogeneous Information Networks (HINs) are vital for modeling complex systems, with link prediction being a key task.
• Existing methods often overlook multi-entity interactions and the causal influence of global graph structure on relation prediction.
• Higher-order relations, involving multiple entities, are common but underexplored in current research.

Purpose of the Study:

• To develop an end-to-end model for higher-order relation prediction in HINs.
• To address the limitations of existing methods by incorporating multi-entity proximity and causal inference.
• To enhance the accuracy and robustness of relation prediction in complex networks.

Main Methods:

• Proposing HINCHOR, a model featuring a higher-order structure encoder to capture multi-entity proximity.
• Implementing a counterfactual data augmentation module to generate synthetic relations based on global structure variations.
• Employing counterfactual learning to estimate causal effects and improve relation prediction.

Main Results:

• HINCHOR demonstrates superior performance compared to state-of-the-art methods on four benchmark datasets.
• The model effectively captures higher-order proximity information and causal relationships within HINs.
• Counterfactual learning enhances the prediction of complex, multi-entity interactions.

Conclusions:

• HINCHOR offers a significant advancement in higher-order relation prediction for Heterogeneous Information Networks.
• The integration of counterfactual learning provides a more robust approach to understanding causal factors in network relations.
• This work opens new avenues for analyzing and predicting complex interactions in real-world systems.