Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph
迈向类人感知：在异构图中学习结构因果模型AI 解读 ↓
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迈向类人感知：在异构图中学习结构因果模型
- 研究问题
- 异构图学习中，如何利用图模式的语义构造可理解变量，并学习任务层面的因果关系以改善解释性与分布外泛化？
- 方法与思路
- 作者提出 HG-SCM，先依据图模式建立语义变量，再结合因果发现技术学习这些变量之间的结构因果关系。
- 材料陈述的贡献
- 摘要报告在三个真实数据集和三类分布外设定中具有较好的预测与泛化表现，学习图的解释也与领域知识相吻合。
- 阅读关注点
- 应核对语义变量的构建方式、因果发现假设、分布外划分和比较公平性，以及图解释与真实因果关系如何区分。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要中的类人及因果解释仍需核查，预测表现不能单独验证因果结构。
原文摘要
Heterogeneous graph neural networks have become popular in various domains. However, their generalizability and interpretability are limited due to the discrepancy between their inherent inference flows and human reasoning logic or underlying causal relationships for the learning problem. This study introduces a novel solution, HG-SCM (Heterogeneous Graph as Structural Causal Model). It can mimic the human perception and decision process through two key steps: constructing intelligible variables based on semantics derived from the graph schema and automatically learning task-level causal relationships among these variables by incorporating advanced causal discovery techniques. We compared HG-SCM to seven state-of-the-art baseline models on three real-world datasets, under three distinct and ubiquitous out-of-distribution settings. HG-SCM achieved the highest average performance rank with minimal standard deviation, substantiating its effectiveness and superiority in terms of both predictive power and generalizability. Additionally, the visualization and analysis of the auto-learned causal diagrams for the three tasks aligned well with domain knowledge and human cognition, demonstrating prominent interpretability. HG-SCM's human-like nature and its enhanced generalizability and interpretability make it a promising solution for special scenarios where transparency and trustworthiness are paramount.