ENRON
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Subgraph Reconstruction Attack | Enron | Precision85.9 | 56 | |
| Dynamic Link Detection | Enron | AP96.31 | 44 | |
| Language Modeling | Enron Dataset | Perplexity3.4 | 39 | |
| Link Prediction | Enron (inductive) | AP97.6 | 37 | |
| Link Prediction | Enron (transductive) | AP99.1 | 28 | |
| Graph Anomaly Detection | Enron | AUC73.9 | 19 | |
| Text Classification | Enron | ACC99.36 | 17 | |
| Dynamic Graph Anomaly Detection | Enron (test) | AUROC0.9989 | 14 | |
| Dynamic Graph Anomaly Detection | Enron S3 setting | AUROC99.87 | 14 | |
| PII Extraction (Personal Names) | Enron (train) | Unique PII Count51,810 | 14 | |
| PII Extraction (Phone Numbers) | Enron (train) | Count of Unique Extracted PII4,766 | 14 | |
| PII Extraction (Email Addresses) | Enron (train) | Unique PII Count12,186 | 14 | |
| Membership Inference Attack | Enron | AUC0.5904 | 12 | |
| Temporal Link Prediction | ENRON historical negative sampling (transductive) | Average Precision0.8314 | 11 | |
| Sparse Tensor Decomposition | ENRON | Fit0.19 | 10 | |
| Multi-label Feature Selection | ENRON | Hamming Loss4.7 | 8 | |
| Multi-label classification | ENRON | Average Precision0.4899 | 8 | |
| Multi-label classification | ENRON | Ranking Loss0.1376 | 8 | |
| Multi-label classification | ENRON | Macro-F166.71 | 8 | |
| Multi-label Feature Selection | ENRON | Coverage60.11 | 8 | |
| Multi-label classification | ENRON | Micro-F10.6686 | 8 | |
| Multi-label classification | ENRON processed image data | F1 Score15.4 | 6 | |
| Binary Classification | Enron | ACC99.05 | 6 | |
| Link Prediction | Enron (last three snapshots) | AUC93.54 | 6 | |
| Formula Prediction | Enron (test) | Formula Score55.8 | 6 |
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