AI, XR, DevOps, graph theory, knowledge graphs, and digital sovereignty. Deep dives, tutorials, and analysis from across the GraphWiz knowledge base.
The GQL deep-dive in our graph research corpus shows query languages are the fastest-growing graph field in 2026. One of the most concrete applied results uses a GQL-style language to trace cyber attacks across provenance graphs of system audit events. Here's the pattern, a worked example, and what it means for practitioners.
The hottest agent research of 2026 is about memory — and the memory is increasingly a graph. This article breaks down the agent-memory-graph wave: persona graphs, experience graphs, self-evolution, and the benchmarks proving graph memory beats flat lists, with real papers and a minimal implementation.
GraphRAG has left the 'can it work?' phase and entered the 'can you trust it?' phase. This article reviews the 2026 shift from hype to hardening — evaluation benchmarks, robustness to knowledge poisoning, and attacks first tested only on vector RAG now hitting graphs.
Agentic GraphRAG is the fastest-moving corner of graph research in 2026. This article breaks down the pattern taxonomy — tool-calling, RL-trained…
Graph foundation models are 2026's most visible graph-machine-learning story: a single pretrained model that can reason over any graph. This article reviews the unification battle — graph-to-table, heterograph transformers, node-agent architectures — the emerging attack literature, and what a GFM actually promises to production teams.
A practical comparison of GraphQL and SPARQL as query layers over knowledge graphs — data model differences, transitive closure gaps, federation…
Knowledge graphs have historically been text-only. Multimodal knowledge graphs add images, audio, 3D and spatiotemporal structure to entities and relations. This article reviews the 2026 wave: multimodal knowledge-graph construction, MLLM-guided completion, multimodal GraphRAG, and the benchmarks now measuring it.
Graph query languages are one of the fastest-growing — and thinnest — cells in the research corpus. This article covers the 2026 GQL standard wave: compositional property-graph languages, NL2GQL, the theory of expressive power, and why the query-language layer is becoming a product surface again.
How the Crystallis framework uses progressive nucleation and semantic annealing to generate coordinated multi-view visualisations from natural language…
How Neo4j's new MCP for Aura brings hosted Model Context Protocol servers to every graph database instance — enabling AI agents to query, explore, and…
How Neo4j Bloom's new Whole-Graph Projections feature bridges graph data science algorithms and interactive visual exploration — enabling analysts to…
How Neo4j Virtual Graph — now in public preview — enables federated queries across relational databases, document stores, and APIs through a unified…
How knowledge graphs solve the public sector's workforce intelligence problem — enabling compound questions across HR systems, skills taxonomies,…
A comprehensive comparison of the four leading graph databases — Neo4j, Amazon Neptune, ArangoDB, and TigerGraph — covering data models, query languages…
The C2RAG framework addresses GraphRAG's two failure modes — retrieval drift from spurious noise and hallucination from incomplete information — using…
How property graphs and graph algorithms detect fraud rings, synthetic identity networks, circular money laundering, and coordinated attacks that…
A practical guide to Neo4j's APOC standard library — covering data conversion, graph refactoring, trigger-based updates, full-text search, and temporal…
A comprehensive guide to Neo4j's APOC standard library — covering data integration, graph refactoring, temporal operations, geospatial queries, and…
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