RCA Ground Truth Labels
The supervision signal for GNN training — what the model learns to predict.
Source File
Label Schema (5 classes)
| Label ID | Name | Count | Nodes |
|---|---|---|---|
| 0 | root_cause | 1 | AGG-NYC-E-01 |
| 1 | primary_symptom | 3 | CSG-NYC-E-01, gNB-NYC-E-047, eNB-NYC-E-047 |
| 2 | secondary_symptom | 3 | CORE-RTR-NYC-01, UPF-NYC-01, AMF-NYC-01 |
| 3 | collateral | 3 | gNB-NYC-E-048, CSG-NYC-E-02, AGG-NYC-E-02 |
| 4 | unaffected | 2 | gNB-NYC-E-049, CSG-NYC-E-03 |
Training Target Formats
The labels are provided in three formats for different GNN tasks:
- Node classification (5-class):
[1, 3, 4, 1, 1, 3, 4, 0, 3, 2, 2, 2] - Binary root cause detection (2-class):
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0] - Root cause ranking (regression): scores from 0.0 to 0.95
Propagation Graph
A directed causal graph showing how the fault propagated from AGG-NYC-E-01 through 9 edges to 9 downstream elements. See rca-labels.json propagation_graph field.
Label Source
Derived from trouble ticket INC00847291, validated by senior transport engineer.