Research map / Graph learning / Knowledge and temporal graphs
Temporal Network Embedding with High-Order Nonlinear Information
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| temporal embedding (HNIP) vs best baseline | HNIP · DBLP link prediction | precision@100: 0.41 -> 0.43 | Helped |
| HNIP vs best baseline | HNIP · Twitter link prediction | precision@100: 0.22 -> 0.27 | Helped |
| HNIP vs CTDNE at larger k | HNIP · Twitter link prediction | precision@5000: 0.117 -> 0.081 | Hurt |
| temporal embedding methods vs static methods | temporal embeddings · DBLP link prediction | precision@k: HNIP 0.43 vs DeepWalk 0.33 at P@100 | Helped |
| HNIP vs static DeepWalk on reconstruction | HNIP · Facebook network reconstruction | precision@10000: 0.469 -> 0.402 | Hurt |
Closest papers by meaning
- Network Structure and Transfer Behaviors Embedding via Deep Prediction Model (AAAI 2019 · similarity 0.88)
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- Simple and Efficient Heterogeneous Temporal Graph Neural Network (NeurIPS 2025 · similarity 0.86)
- Improving Temporal Link Prediction via Temporal Walk Matrix Projection (NeurIPS 2024 · similarity 0.84)
- Continuous-Time Graph Representation with Sequential Survival Process (AAAI 2024 · similarity 0.84)
- Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs (NeurIPS 2024 · similarity 0.84)
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