Understanding Graph Embedding Methods and Their Applications

Computer Science

• Embedding Method 100%
• Graph Analytics 80%
• Computational Cost 20%
• Complex Networks 20%
• Deep Learning 20%
• Neural Network 20%
• Open Source Software 20%
• Traditional Method 20%
• Node Classification 20%
• Uncertainty Estimation 20%
• Fundamental Concept 20%
• Supplementary Material 20%
• High Dimensionality 20%
• Memory Requirement 20%
• Link Prediction 20%
• Random Walk 20%
• Implementation Detail 20%
• Community Detection 20%

Keyphrases

• Graph Embedding 100%
• Graph Analytics 44%
• High Dimension 11%
• Graph Structure 11%
• Sparse Graphs 11%
• Diverse Applications 11%
• Structure-property Relationships 11%
• Deep Learning Methods 11%
• Node Classification 11%
• Uncertainty Estimation 11%
• Random Walk 11%
• Gaussian Distribution 11%
• Neural Network Method 11%
• Memory Requirements 11%
• Distribution-based 11%
• Link Prediction 11%
• Embedding Vector 11%
• Implementation Details 11%
• Community Detection 11%
• Small Dimension 11%
• Vector Space 11%
• Latent Space 11%
• Node Similarity 11%
• High Computational Cost 11%
• Dynamic Graph Embedding 11%
• Analytic Task 11%
• Control of Complex Networks 11%
• Node Properties 11%
• Heterogeneous Characteristics 11%
• Continuous Vector Space 11%