Exploring knowledge graphs, graph theory, graph visualization, and their intersection with artificial intelligence.
A comprehensive comparison of the four leading graph databases — Neo4j, Amazon Neptune, ArangoDB, and TigerGraph — covering data models, query languages, scalability, deployment options, and a decision framework for choosing the right graph database for your use case.
The C2RAG framework addresses GraphRAG's two failure modes — retrieval drift from spurious noise and hallucination from incomplete information — using constraint-checked retrieval with sufficiency verification. It consistently outperforms baselines by 3.4% EM and 3.9% F1.
How property graphs and graph algorithms detect fraud rings, synthetic identity networks, circular money laundering, and coordinated attacks that traditional methods miss.
A practical comparison of the four leading graph databases — Neo4j, Amazon Neptune, ArangoDB, and TigerGraph — covering architecture, query languages, performance, licensing, and which to choose for your use case.
Production frameworks for governing autonomous agents: bounded autonomy, inter-agent security, jailbreak prevention, and compliance under the EU AI Act.
How 3B-30B parameter models are moving to edge devices — from smartphones to industrial systems — driven by NPU hardware, quantization advances, and privacy requirements.
The C2RAG framework addresses GraphRAG's two failure modes — retrieval drift from spurious noise and hallucination from incomplete information — using constraint-checked retrieval with sufficiency verification. It consistently outperforms baselines by 3.4% EM and 3.9% F1.
How property graphs and graph algorithms detect fraud rings, synthetic identity networks, circular money laundering, and coordinated attacks that traditional methods miss.
A practical guide to detecting financial fraud with Neo4j knowledge graphs — graph data models for fraud, Cypher queries for suspicious pattern detection, and GDS pipeline integration with production-grade examples.