Knowledge graphs sessions 82-84: Groth16 witness builder, 5 new MCP tools, and comprehensive graph analytics (v3.22.36–3.22.38)

[Copilot]

Continues the incremental delivery of deferred v4.0+ knowledge-graph roadmap items — ZKP/Groth16 integration depth, GNN link prediction, federated queries, and observable graph analytics — all exposed via the MCP server tool layer.

Session 82 — Groth16 TDFOL witness builder (v3.22.36)

query/groth16_kg_witness.py (new):

• KGAtomEncoder — normalises KG entity/relationship strings to valid TDFOL_v1 atom identifiers
• KGWitnessBuilder — builds TDFOL_v1 witness_inputs dicts for entity_exists, path_exists, entity_property, and query_answer_count proof types

query/groth16_bridge.py (updated):

• KGEntityFormula.to_tdfol_atoms() — classmethod emitting TDFOL atom list for a KG entity tuple

from ipfs_datasets_py.knowledge_graphs.query.groth16_kg_witness import KGWitnessBuilder
builder = KGWitnessBuilder(kg)
witness = builder.build_entity_exists_witness("alice")
# {"entity_id": "alice", "entity_type": "person", "entity_hash": "..."}

Session 83 — 3 new MCP tools: GNN embed, ZKP prove, federated query (v3.22.37)

Tool	Delegates to
graph_gnn_embed	GraphNeuralNetworkAdapter.compute_embeddings() — GRAPH_CONV / SAGE / ATTENTION; top-k similar entities
graph_zkp_prove	KGZKProver + optional KGWitnessBuilder — entity/path/property/count proofs with TDFOL_v1 witness
graph_federate_query	FederatedKnowledgeGraph — cross-graph entity resolution (type+name / exact name / property match); optional merge

graph_tools/__init__.py: 19 → 22 tools.

Session 84 — 2 new MCP tools: KG analytics and link prediction (v3.22.38)

Tool	Description
graph_analytics	Quality metrics (KnowledgeGraphExtractor.compute_extraction_quality_metrics), completion suggestions (KnowledgeGraphCompleter), and topology stats (type distributions, degree min/max/avg, source-only / sink-only counts)
graph_link_predict	Cosine similarity of GNN node embeddings via GraphNeuralNetworkAdapter.link_prediction_score(); optional top-k candidate ranking

# Example: full analytics pass
r = await manager.analytics(kg_data=kg.to_dict())
r["quality_metrics"]["avg_entity_confidence"]  # 0.85
r["topology"]["degree_stats"]                  # {"min": 0, "max": 4, "avg": 1.5}
r["missing_relationships"][0]                  # {"source_id": "alice", "rel_type": "knows", ...}

# Example: link prediction
r = await manager.link_predict("alice", "bob", kg_data=kg.to_dict(),
                                top_candidates=["bob", "acme", "solo"])
r["score"]          # 0.87  → "likely"
r["top_predictions"]  # [{"entity_id": "bob", "score": 0.87}, ...]

graph_tools/__init__.py: 22 → 24 tools.
Degree computation uses sparse dict + .get() (no pre-allocation for large sparse graphs).

Supporting changes

• KnowledgeGraphManager gained 5 new async methods: gnn_embed, zkp_prove, federate_query, analytics, link_predict
• DEFERRED_FEATURES.md P13–P15 (§27–§33) marked delivered
• ROADMAP, CHANGELOG, MASTER_STATUS, IMPROVEMENT_TODO updated to v3.22.38
• 43 new tests (session 84); 286 pass across sessions 80–84

---

💬 We'd love your input! Share your thoughts on Copilot coding agent in our 2 minute survey.