Deep graph kernel point processes over networks

arXiv:2306.11313 · stat.ML, cs.LG · Submitted 2023-06-20 · Read on arXiv

stat.ML, cs.LG

Submitted: 2023-06-20

Updated: 2026-08-29

Terminology

• GNNs — 19× in this paper · explained in 2 episodes
• Loss Function — 14× in this paper · explained in 2 episodes
• Graph Neural Networks — 13× in this paper
• Transformer — 10× in this paper · explained in 2 episodes
• Interpretability — 8× in this paper · explained in 15 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Ablation study — 6× in this paper · explained in 1 episode
• Adjacency Matrix — 6× in this paper · explained in 1 episode
• Least Square — 4× in this paper
• Negative Log-Likelihood — 4× in this paper
• Objective Function — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptive Graph — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Geometric Hawkes Process — 1× in this paper
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes

Sources

• Adam: A Method for Stochastic Optimization
• A Survey on Graph Structure Learning: Progress and Opportunities

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