Accelerating Robust Graph Representation Learning: A High-Performance Dual-Stream Contrastive Framework

The Cora citation network, a benchmark dataset with 2,708 nodes and 5,429 edges spread across 7 classes, is used to evaluate the Graph Convolutional Network (GCN) for node classification in this paper. We add an 8-dimensional Laplacian Positional Encoding (LPE) to the original 1,433 - dimensional node features to improve the model's awareness of the graph's topology. Our framework uses a dual-stream GCN pipeline with feature diffusion, attention-based fusion, and a contrastive learning goal for enhanced robustness to process this improved feature set. The architecture, which was trained using an Adam optimizer and implemented with an early stopping function to avoid overfitting, performs well, resulting in a test accuracy of 80.9% for its output. Qualitative analysis, using t-SNE visualizations and a confusion matrix, confirms the model's ability to learn effective representations. The results validate that integrating structural encodings improves GCN performance for graph-based machine learning.