Unicosys Hypergraph Knowledge Model
A trainable knowledge graph embedding model encoding the unified evidence hypergraph for Case 2025-137857.
Model Description
This model encodes a unified hypergraph linking financial transactions, email communications, legal evidence, and entity relationships into a single trainable knowledge representation.
Architecture
| Component | Details |
|---|---|
| Node Embedding | 128-dim structural + 256-dim text |
| Hidden Dimension | 256 |
| Text Encoder | 2-layer Transformer, 4 heads |
| Graph Attention | 2-layer GAT, 4 heads |
| Link Predictor | 2-layer MLP with margin ranking loss |
| Total Parameters | 34,762,497 |
Knowledge Graph Statistics
| Metric | Count |
|---|---|
| Total Nodes | 198,019 |
| Total Edges | 13,415 |
| Cross-Links | 3,664 |
| Entities | 16 |
| Emails | 197,993 |
| Financial Documents | 0 |
| Timeline Events | 10 |
| LEX Schemes | 0 |
| Legal Filings | 0 |
Subsystems
| Subsystem | Nodes |
|---|---|
| Core (Entities) | 16 |
| Fincosys (Financial) | 0 |
| Comcosys (Communications) | 197,993 |
| RevStream1 (Evidence) | 0 |
| Ad-Res-J7 (Legal) | 10 |
Training
The model can be fine-tuned on link prediction tasks:
from model.unicosys_model import UnicosysHypergraphModel, UnicosysConfig
model = UnicosysHypergraphModel.from_pretrained("hyperholmes/unicosys-hypergraph")
# ... prepare training data ...
# model.forward(node_ids, node_type_ids, subsystem_ids, edge_index, edge_type_ids,
# pos_edge_index=pos, neg_edge_index=neg, labels=labels)
Files
model.safetensors— Model weightsconfig.json— Model configurationgraph_data.safetensors— Encoded graph tensors (nodes, edges)tokenizer.json— Character-level tokenizer for node labelsnode_id_mapping.json— Node ID string to integer index mappingmodel_summary.json— Compact statistics summary
Source
Generated by the Unicosys intelligence pipeline.