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Towards Training and Inference of Graph Neural Networks in Graph Databases

arXiv cs.LG ↗ · 2d ago Cached

该论文研究在图数据库中直接进行图神经网络的训练与推理,展示使用Cypher执行采样、特征检索和完整前向传播的可行性,优化后训练速度提升约2.5倍,推理速度比冷加载图到内存快10倍以上。

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#neo4j

Massive Parallel Imports in Neo4j Without Deadlock and Lock Contention

Lobsters Hottest ↗ · 2026-09-24 Cached

The article proposes an improved method for massive parallel imports in Neo4j, using hash-based partitioning and the k-1 coloring algorithm to avoid deadlock and lock contention during data loading.

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@neo4j: A practical, engineering-focused approach to building a semantic layer on Neo4j with ontology at the core, with @Barras…

X AI KOLs Timeline ↗ · 2026-08-28 Cached

This webinar provides a practical engineering-focused approach to building a semantic layer on Neo4j using ontology, emphasizing benefits for AI-driven applications and data consistency.

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@Sprytixl: ANTHROPIC'S LEAD ENGINEER WON A $1.2M BONUS FOR A SYSTEM THAT TURNS ANY DATA CHAOS INTO A GRAPH IN 8 STEPS raw chaos in…

X AI KOLs Timeline ↗ · 2026-07-26 Cached

An Anthropic lead engineer's 8-step pipeline transforms chaotic data into a self-updating graph, boosting productivity by 42% and winning a $1.2M bonus.

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#neo4j

@KirkDBorne: Graph Algorithms for Data Science: http://amzn.to/4s41GJ5 I have said this for years: "All the world is a graph!" The n…

X AI KOLs Timeline ↗ · 2026-07-25 Cached

Promotion of the book 'Graph Algorithms for Data Science' which teaches graph algorithms and their applications using Neo4j, covering topics like knowledge graphs, social network analysis, and node embeddings.

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#neo4j

Dunya Kirkali - Your organization as a Graph

Lobsters Hottest ↗ · 2026-07-04 Cached

A talk from WeAreDevelopers World Congress 2026 on modeling organizations as graphs to reveal hidden networks where work actually happens, alongside related videos on graph databases, knowledge graphs, and developer conferences.

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#neo4j

@qdrant_engine: Looking to Build GraphRAG? Start with This Practical Guide from our star, Pavan In this practical guide, Pavan demonstr…

X AI KOLs Timeline ↗ · 2026-07-01 Cached

This article provides a practical guide to building a GraphRAG system using LangExtract, Neo4j, Qdrant, and Ollama, combining entity extraction, knowledge graphs, and vector search for context-aware retrieval.

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#neo4j

My agent kept forgetting who 'Karpathy' was between sessions. Here's the architecture that fixed it

Reddit r/AI_Agents ↗ · 2026-05-20

A developer shares an architecture using Neo4j knowledge graphs with typed entities and deduplication to solve the problem of AI agents forgetting entity identity across sessions, moving beyond flat files and vector stores.

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Why Vector RAG fails for AI coding agents at scale (And how I used a Neo4j graph to fix it)

Reddit r/ArtificialInteligence ↗ · 2026-05-18

A new open-source tool called Writ uses a hybrid retrieval pipeline with BM25, ONNX vectors, and Neo4j graph traversals to provide context rules for AI coding agents, reducing token bloat by 726x and enforcing plan approval via bash hooks.

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We tried vectors, ASTs, and brute-force context stuffing for code retrieval. Graphs with LLM-generated semantics worked best. Here's what we learned.

Reddit r/LocalLLaMA ↗ · 2026-05-10

The authors detail their experience building a code indexing system, concluding that graph-based retrieval with LLM-generated semantics outperforms vector embeddings and pure AST parsing. They open-sourced the system, Bytebell, which uses Neo4j to store semantic context for efficient and precise code retrieval.

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