Curriculum-guided graph self-augmentation: A progressive deepening framework for GNNs.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Graph Theory

Background:

• Graph Neural Networks (GNNs) excel at graph-structured data but are hindered by over-smoothing in deep architectures.
• Over-smoothing limits GNNs' ability to capture long-range node dependencies due to uniform aggregation weights.
• Insufficient graph data information often prevents the effective use of deep GNNs.

Purpose of the Study:

• To propose a progressive deepening framework, Curriculum-Guided Graph Self-Augmentation (CGGSA), for GNNs.
• To overcome the limitations of shallow GNN architectures caused by over-smoothing.
• To enhance the performance of GNNs in node classification tasks by enabling deeper information aggregation.

Main Methods:

• CGGSA employs a progressive deepening strategy, starting with low-order neighborhood aggregation.
• The framework uses learned guidance from simpler representations to augment graph structure and node features.
• Aggregation depth is gradually increased to capture high-order dependencies, complemented by a class center separation loss.

Main Results:

• CGGSA effectively alleviates the over-smoothing problem in GNNs.
• The proposed method enables deeper GNN architectures to learn complex, long-range interactions.
• Experiments on eleven benchmarks show CGGSA achieves competitive or superior performance in node classification.

Conclusions:

• CGGSA is a viable framework for training deeper GNNs by progressively increasing aggregation depth.
• The self-augmentation strategy and class center separation loss enhance node representation separability and model performance.
• This approach significantly improves GNN capabilities for complex graph mining tasks.