Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph (2312.05757v1)

Abstract: 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.

• The paper introduces HG-SCM, a novel approach that uses structural causal models to mimic human perception in heterogeneous graph analysis.
• It employs causal discovery to focus on causally relevant variables, achieving superior precision and stability in out-of-distribution scenarios.
• The transparent causal diagrams provide intuitive insights that align model predictions with human reasoning, enhancing trust and applicability.

Introduction to Heterogeneous Graph Modeling

Today's systems are inundated with vast networks constructed from different types of data entities and their complex interrelations. Such networks, known as heterogeneous graphs, are pervasive and hold immense potential for applications like social network analysis, recommendation systems, and knowledge graphs. Resolving problems like node property prediction within these graphs is vital for extracting valuable insights. Traditional models, despite remarkable progress, grapple with challenges related to generalizability—the ability to perform accurately on unseen data—and interpretability—providing comprehensible reasoning behind their predictions.

A New Approach: HG-SCM

A recent innovation, termed HG-SCM (Heterogeneous Graph as Structural Causal Model), addresses these challenges head-on. This methodology models the graphs by closely mimicking human cognitive processes, capturing how we perceive tasks and decide based on causally-related factors. Unlike previous methods focusing solely on data associations, HG-SCM constructs variables based on the semantics of the graph and leverages advanced causal discovery techniques to infer task-level causal relationships among these variables. As a result, HG-SCM can automatically identify and focus predictions on variables most likely to cause the properties of interest, thus providing a clear interpretation aligned with human reasoning.

Empirical Evidence of Superiority

The effectiveness of HG-SCM has been solidly validated against state-of-the-art models across various real-world datasets. The evaluation moves beyond standard settings, where data distributions in training and testing sets are identical, and extends to out-of-distribution (o.o.d) conditions that present shifts in data distributions—a more challenging but realistic scenario. HG-SCM demonstrates compelling adaptability to such conditions and outperforms other methods in both precision and stability. This reaffirms that it successfully avoids relying on misleading spurious correlations that can be prevalent in training data.

The Significance of Interpretability

One of the most exciting benefits of HG-SCM is its transparency. The auto-learned causal diagrams render a readable map of connections aligning with domain knowledge. For instance, in an academic network, the model's conclusions concerning an author's research area were guided by meaningful causal links such as the venues they published in and the content of their work—intuitive inferences which humans are likely to make. This ability not only enhances trust in the application but also provides a basis for refining and questioning the model, ensuring that its logic can withstand rigorous scrutiny.

Looking Ahead

HG-SCM's heralds an important stride forward in both the theory and application of heterogeneous graph neural networks. It paves the way for more reliable, understandable, and robust decision-making tools. The future work will focus on incorporating even more refined causal techniques and formally evaluating model interpretability. As graphs underpin an ever-expanding array of systems and decisions, methodologies like HG-SCM could soon be at the cornerstone of understanding and managing the complex web of relationships and interactions in diverse areas of our digital lives.