Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning.

Area of Science:

• Machine Learning
• Graph Representation Learning

Background:

• Graph neural networks (GNNs) are effective for node-level tasks but struggle with initial feature representation.
• Poor initialization leads to slower convergence and training instability in GNNs.

Purpose of the Study:

• To enhance GNN performance by introducing a statistically grounded, structure-aware node feature initialization.
• To develop a framework that improves GNN efficiency, stability, and accuracy.

Main Methods:

• Leveraged a one-hot graph encoder embedding (GEE) for high-quality node feature initialization.
• Integrated GEE into standard GNNs to create the GEE-powered GNN (GG) framework.
• Introduced GG-C by concatenating GG and GEE outputs for node classification.

Main Results:

• GG framework demonstrated consistent performance gains in unsupervised and supervised settings.
• GG-C achieved 10-50% accuracy improvements in node classification across various datasets.
• The proposed initialization significantly improved GNN efficiency and stability.

Conclusions:

• Principled, structure-aware initialization is crucial for advancing graph neural network architecture.
• GEE-powered GNNs effectively exploit graph topology from the outset, enhancing overall performance.
• The GG framework offers a robust solution for improving GNNs in diverse applications.