Towards human-like perception: Learning structural causal model in heterogeneous graph
Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph
Zhuoren Jiang
In recent years, the growing demand for modeling complex systems has brought heterogeneous graph neural networks (HGNNs) into the spotlight. However, existing methods often suffer from fixed inference processes and spurious correlations, limiting their interpretability and generalization ability. To address these challenges, this study proposes a novel heterogeneous graph learning framework that simulates human perception and decision-making processes, enhancing both predictive performance and interpretability.
—Heterogeneous graph learning is paving the way for new possibilities in modeling and analyzing complex systems—
Key Research Approach
The proposed method is built upon the following key steps:
- Semantic Variable Construction: Semantic variables that are intuitive to humans, such as papers, authors, and conferences, are extracted using graph patterns and meta-paths.
- Causal Relationship Discovery: Employing structural causal modeling, the method automatically identifies causal relationships between variables and leverages these to make predictions.
- Task Prediction: Through inverse mapping techniques, causal relationships among semantic variables are translated into predictions for the target task.
Datasets and Experimental Validation
The study validates its approach on three public datasets (DBLP, ACM, and IMDB), encompassing diverse scenarios such as academic, social, and movie recommendation domains:
- DBLP: Predicting an author’s research domain.
- ACM: Predicting the category of academic papers.
- IMDB: Predicting movie genres.
By introducing three types of biases (homophily, degree distribution, and feature distribution), the study tests the model’s generalization capability under varied data distributions. Experimental results demonstrate that the proposed model outperforms existing methods in stability and generalization across all scenarios.
Key Findings
1.Generalization: The model maintains high predictive performance under various biased conditions, showcasing exceptional adaptability.
2.Interpretability: The causal graphs generated by the model provide clear insights into the relationships between variables. For example, the model captures the intuitive correlation between an author’s research domain and the conferences where their work is published, aligning closely with expert knowledge.
Practical Implications
This framework holds great potential for applications in domains such as technology innovation, financial analysis, and policy evaluation. For instance, it can aid decision-making in technological advancements by utilizing causal graphs or improve transparency in financial analyses by explaining key influencing factors. Furthermore, its high interpretability enables researchers to refine the model’s reasoning logic, enhancing trustworthiness in practical deployments.
Conclusion
Heterogeneous graph learning is paving the way for new possibilities in modeling and analyzing complex systems. By integrating causal inference techniques with HGNNs, this study not only achieves accurate task predictions but also deepens understanding of the reasoning process, offering a fresh perspective for applying artificial intelligence to real-world challenges.
This article is based on the following paper: Lin, T., Song, K., Jiang, Z., Kang, Y., Yuan, W., Li, X., … & Liu, X. (2024). Towards human-like perception: Learning structural causal model in heterogeneous graph. Information Processing & Management, 61(2), 103600.
Cite this article in APA as: Jiang, Z. Towards human-like perception: Learning structural causal model in heterogeneous graph. (2024, December 12). Information Matters, Vol. 4, Issue 12. https://informationmatters.org/2024/12/towards-human-like-perception-learning-structural-causal-model-in-heterogeneous-graph/
