Research map / Graph learning / GNN architectures
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Evolve GCN parameters over time instead of using a static GCN | EvolveGCN-O vs GCN · SBM | MAP: 0.1987 -> 0.1989 | Helped |
| Evolve GCN parameters over time instead of using a static GCN | EvolveGCN-O vs GCN · UCI | MAP: 0.0251 -> 0.0270 | Helped |
| Evolve GCN parameters over time instead of using a static GCN | EvolveGCN-H vs GCN-GRU · AS | MAP: 0.0713 -> 0.1534 | Helped |
| Use EvolveGCN rather than static GCN-related approaches | EvolveGCN link prediction · BC-OTC | MAP: 0.0220 vs 0.0028 | Hurt |
| Use EvolveGCN rather than static GCN-related approaches | EvolveGCN link prediction · BC-Alpha | MAP: 0.1100 vs 0.0049 | Hurt |
| Use evolving GCN parameters rather than a single shared GCN | EvolveGCN-H and EvolveGCN-O · BC-OTC, BC-Alpha, Reddit | F1 | Helped |
| Use dynamic modeling for node classification | EvolveGCN-O vs static GCN · Elliptic | minority F1 | Helped |
| Use EvolveGCN-O rather than GCN-GRU | Elliptic node classification · Elliptic | minority F1 | Hurt |
| Rely on historical dynamics to handle an abrupt unseen event | Dynamic graph models · Elliptic, step 43 | minority F1 | Hurt |
Closest papers by meaning
- Temporal Generalization Estimation in Evolving Graphs (ICLR 2024 · similarity 0.90)
- How Graph Neural Networks Learn: Lessons from Training Dynamics (ICML 2024 · similarity 0.90)
- Generalizing CNNs to graphs with learnable neighborhood quantization (NeurIPS 2024 · similarity 0.89)
- Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence (ICML 2025 · similarity 0.89)
- Node-Time Conditional Prompt Learning in Dynamic Graphs (ICLR 2025 · similarity 0.88)
- On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective (NeurIPS 2025 · similarity 0.88)
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