Transform messy graph data into clean Gaussian distributions for improved ML performance
SGEC (Subgraph Gaussian Embedding Contrast) maps graph nodes into Gaussian space to create better embeddings for machine learning tasks.
Gaussian embeddings: A smarter way to understand complex graph relationships
https://arxiv.org/abs/2411.07150
🎯 Original Problem:
Graph representation learning faces challenges in effectively encoding high-dimensional graph data into low-dimensional vectors while preserving structural information. Current contrastive learning methods produce uneven node distributions with sharp boundaries and incorrect clustering.
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🔧 Solution in this Paper:
→ The paper introduces SGEC (Subgraph Gaussian Embedding Contrast) that maps subgraphs to a structured Gaussian space
→ Uses a combination of GraphSAGE and GAT layers to generate subgraph features while preserving topology
→ Implements KL divergence regularization to guide node features toward Gaussian distribution
→ Employs Wasserstein and Gromov-Wasserstein distances to measure subgraph similarities during contrastive learning
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💡 Key Insights:
→ Controlling distribution of generated contrastive pairs is crucial for effective graph representation
→ Structured Gaussian space helps prevent mode collapse in embeddings
→ Combining feature-level and structural similarities through optimal transport improves robustness
→ Adaptive subgraph mapping preserves essential graph characteristics
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📊 Results:
→ Achieved highest accuracies on Squirrel (56.39%), Cornell (94.58%), and Texas (92.38%) datasets
→ Outperformed state-of-the-art methods like GREET, GRACE, and GSC
→ Demonstrated competitive performance across 8 benchmark datasets
→ Ablation studies validated effectiveness of Gaussian regularization over dropout
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