Research map / Graph learning / Graph algorithms and clustering
Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use class-specific decay rates rather than one shared rate | TRNNGCN vs RNNGCN · Simulated dynamic SBM | accuracy: 5% | Helped |
| Use class-specific decay rates rather than one shared rate | TRNNGCN vs RNNGCN · Simulated dynamic SBM | F1-score: 10% | Helped |
| Apply optimized decay to historical edges instead of accumulating all past edges uniformly | RNNGCN and TRNNGCN vs static GCN and Spectral Clustering · Simulated dynamic SBM | accuracy | Helped |
| Use class-specific rather than shared decay rates | TRNNGCN vs RNNGCN · DBLP-3 | F1: 66.7 -> 73.8 | Helped |
| Use class-specific rather than shared decay rates | TRNNGCN vs RNNGCN · DBLP-5 | F1: 58.6 -> 63.5 | Helped |
| Use class-specific rather than shared decay rates | TRNNGCN vs RNNGCN · Reddit | AUC: 20.5 -> 25.6 | Helped |
| Use class-specific rather than shared decay rates | TRNNGCN vs RNNGCN · Brain | AUC: 38.6 -> 42.4 | Helped |
| Use class-specific rather than shared decay rates | TRNNGCN vs RNNGCN · Reddit | accuracy: 33.6 -> 33.6 | No clear effect |
| Use class-specific rather than shared decay rates | TRNNGCN vs RNNGCN · Brain | F1: 84.7 -> 85.7 | Hurt |
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