Research map / Graph learning / Knowledge and temporal graphs
Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use finer-grained daily rather than monthly snapshots | Temporal GNNs for link prediction on the NFT transaction graph · NFT transaction graph, fixed split | AUC: 86.23$\pm$0.85 (monthly) -> 95.93$\pm$0.15 (daily) | Helped |
| Evaluate across all available snapshots with live-update rather than only the final 20% | Temporal GNNs for link prediction · NFT transaction graph, daily snapshots | AUC: 95.93$\pm$0.15 -> 90.47$\pm$0.66 | Hurt |
| Use RapidFlow rather than SymBi for continuous subgraph matching | NFT wash-trading pattern p1 · NFT wash-trading patterns p1 | query time: 1.22$\times$10$^4$ -> 5.93$\times$10$^2$ | Helped |
| Use RapidFlow rather than Graphflow for continuous subgraph matching | NFT wash-trading pattern p5 · NFT wash-trading patterns p5 | query time: 4.24$\times$10$^5$ -> 5.84$\times$10$^2$ | Helped |
| Use RapidFlow rather than TurboFlux for continuous subgraph matching | NFT wash-trading pattern p4 · NFT wash-trading patterns p4 | query time: 6.06$\times$10$^4$ -> 6.45$\times$10$^2$ | Helped |
| Use finer-grained daily rather than monthly snapshots | Temporal GNNs for node classification on the NFT transaction graph · NFT transaction graph, fixed split | Accuracy: 47.96$\pm$2.69 (monthly) -> 51.02$\pm$2.01 (daily) | Mixed |
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