Introducing Diminutive Causal Structure into Graph Representation Learning

TL;DR

This paper proposes a novel method for graph neural networks that leverages diminutive causal structures within graph data to improve learning accuracy, supported by theoretical analysis and empirical results.

Contribution

It introduces a new approach to incorporate specialized diminutive causal structures into GNN training, enhancing model performance and understanding.

Findings

Significant performance improvements across multiple datasets.

GNNs tend to converge towards specific diminutive causal structures.

Theoretical analysis confirms the effectiveness of the proposed method.

Abstract

When engaging in end-to-end graph representation learning with Graph Neural Networks (GNNs), the intricate causal relationships and rules inherent in graph data pose a formidable challenge for the model in accurately capturing authentic data relationships. A proposed mitigating strategy involves the direct integration of rules or relationships corresponding to the graph data into the model. However, within the domain of graph representation learning, the inherent complexity of graph data obstructs the derivation of a comprehensive causal structure that encapsulates universal rules or relationships governing the entire dataset. Instead, only specialized diminutive causal structures, delineating specific causal relationships within constrained subsets of graph data, emerge as discernible. Motivated by empirical insights, it is observed that GNN models exhibit a tendency to converge towards such specialized causal structures during the training process. Consequently, we posit that the introduction of these specific causal structures is advantageous for the training of GNN models. Building upon this proposition, we introduce a novel method that enables GNN models to glean insights from these specialized diminutive causal structures, thereby enhancing overall performance. Our method specifically extracts causal knowledge from the model representation of these diminutive causal structures and incorporates interchange intervention to optimize the learning process. Theoretical analysis serves to corroborate the efficacy of our proposed method. Furthermore, empirical experiments consistently demonstrate significant performance improvements across diverse datasets.