Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework (2103.02885v1)

Abstract: Semi-supervised learning on graphs is an important problem in the machine learning area. In recent years, state-of-the-art classification methods based on graph neural networks (GNNs) have shown their superiority over traditional ones such as label propagation. However, the sophisticated architectures of these neural models will lead to a complex prediction mechanism, which could not make full use of valuable prior knowledge lying in the data, e.g., structurally correlated nodes tend to have the same class. In this paper, we propose a framework based on knowledge distillation to address the above issues. Our framework extracts the knowledge of an arbitrary learned GNN model (teacher model), and injects it into a well-designed student model. The student model is built with two simple prediction mechanisms, i.e., label propagation and feature transformation, which naturally preserves structure-based and feature-based prior knowledge, respectively. In specific, we design the student model as a trainable combination of parameterized label propagation and feature transformation modules. As a result, the learned student can benefit from both prior knowledge and the knowledge in GNN teachers for more effective predictions. Moreover, the learned student model has a more interpretable prediction process than GNNs. We conduct experiments on five public benchmark datasets and employ seven GNN models including GCN, GAT, APPNP, SAGE, SGC, GCNII and GLP as the teacher models. Experimental results show that the learned student model can consistently outperform its corresponding teacher model by 1.4% - 4.7% on average. Code and data are available at https://github.com/BUPT-GAMMA/CPF

Effective Knowledge Distillation from Graph Neural Networks

The paper "Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework" presents a novel approach to enhance semi-supervised learning on graph-structured data. It builds upon the strengths of Graph Neural Networks (GNNs) and traditional methods like label propagation by designing a student model that can both interpret and outperform its GNN teacher models.

Overview

Graph neural networks (GNNs) have recently shown superior performance over traditional methods such as label propagation in classifying nodes on graphs, leveraging their capacity to integrate graph structure and node features through sophisticated architectures. However, the entanglement of graph topology, node features, and complex projection mechanisms in GNNs can lead to overly complex prediction processes, which do not fully utilize simple yet valuable prior knowledge inherent in the data. In particular, traditional assumptions, such as structurally correlated nodes sharing the same label, are often underexploited.

The authors propose a new framework that applies knowledge distillation to extract and refine the knowledge from an arbitrary pretrained GNN model, referred to as the teacher. This knowledge is then injected into a specifically designed student model. The student incorporates two elementary prediction mechanisms—label propagation and feature transformation—which inherently leverage structure-based and feature-based prior knowledge respectively.

Experimental Framework

The framework's effectiveness was validated through extensive experiments on five public benchmark datasets, integrating knowledge from seven different GNN models including GCN, GAT, APPNP, SAGE, SGC, GCNII, and GLP as teacher models. The results consistently demonstrate that the student models can outperform their respective GNN teacher models in classification tasks. Notably, students achieved improvements in accuracy ranging from 1.4% to 4.7% over their teachers.

Key Components of the Student Model

1. Parameterized Label Propagation (PLP): Traditional label propagation assigns equal weight to all node neighbors when propagating labels. The paper enhances this method by introducing confidence scores, allowing nodes with more reliable predictions to have higher influence in the propagation process.

2. Feature Transformation (FT): The ability of a node's features to predict its label is exploited through a 2-layer MLP. This approach complements the structure-based predictions and contributes to the robustness and accuracy of the student model.

The student model is implemented as a trainable combination of these two components, resulting in a prediction process that is both effective and interpretable.

Implications

The proposed framework offers several key benefits:

• Improved Predictive Accuracy: By enabling the student model to synthesize both graph-based and feature-based knowledge, predictions become more robust compared to those by standalone GNN models.
• Interpretability: The simplified prediction mechanisms offer insights into how decisions are made, which is an advantage over more opaque GNN architectures.
• Compatibility and Flexibility: It presents a framework compatible with different architectural designs of GNNs, as evidenced by the successful application on diverse models including label propagation-centric GNN architectures like GLP.

Future Directions

The promising results suggest several avenues for future research:

• Generalization Across Domains: Further exploration of the framework's applicability to other graph-based tasks such as clustering could expand its utility beyond semi-supervised classification.
• Iterative Knowledge Exchange: Investigating iterative processes where the teacher model is updated with feedback from the student could further enhance model performance.
• Hybrid and Layer-wise Distillation: Combining layer-specific distillation approaches might unlock novel ways to enhance each stage of neural message passing at a granular level.

This paper exemplifies the power of synthesizing established techniques with modern deep learning methods, offering pathways to refine and expand the capabilities of graph analytics. The paper provides a solid foundation for advancing interpretable and efficient graph neural network applications.