AWS disclosed that Resilient Network Graphs, a flat network architecture based on quasi-random graph theory, is now the default for most new data center builds. The design replaces fat-tree...

PostgreSQL 19 adds GRAPH_TABLE, letting you query property graphs with Cypher-like pattern matching over your existing relational tables.

By Oleksii Tkachuk, Kartik Sathyanarayanan, Rajiv ShringiIntroductionNetflix has a diverse range of graph use cases, each serving specific business needs with unique functionality and performance...

Netflix has developed a graph-based architecture for managing machine learning systems, called the Model Lifecycle Graph. This system maps interconnections between datasets, models, features, and...

Saish Sali, Nipun Kumar, Sura ElamuruguIntroductionAs Netflix has grown, machine learning continues to support our ability to deliver value to members and drive excellence across multiple areas of...

Mariia Bulycheva discusses the transition from classic deep learning to GNNs for Zalando's landing page. She explains the complexities of converting user logs into heterogeneous graphs, the...

我们将更新服务，限制应用程序修改非草稿邮件的敏感属性（如主题和正文）。只有具备特定权限的应用才能进行修改，新的限制将于2026年12月31日生效。建议开发者尽快申请更高权限，以确保顺利过渡。

Netflix engineers built Graph Abstraction, a high-throughput platform managing 650 TB of graph data with millisecond latency. Supporting services from Netflix Gaming’s social graphs to operational...

Prasanna Vijayanathan and Renzo Sanchez-Silva, both Engineers at Netflix, presented “Ontology‐Driven Observability: Building the E2E Knowledge Graph at Netflix Scale” at QCon London 2026, where...

The AI Evolution of Graph Search at Netflix: From Structured Queries to Natural LanguageBy Alex Hutter and Bartosz BalukiewiczOur previous blog posts (part 1, part 2, part 3) detailed how...

I have a graph: undirected, unweighted, no leaves, no disconnected edges or vertices. The graph is populated with vertices of 3 types: A, B, C. Let's say, I hold a vertex of type A, denoted as...

How and Why Netflix Built a Real-Time Distributed Graph: Part 1 — Ingesting and Processing Data Streams at Internet ScaleAuthors: Adrian Taruc and James DaltonThis is the first entry of a...

本文探讨了如何将Neo4j图数据库与大语言模型（LLM）结合，构建问答系统。通过知识图谱存储信息，LLM生成Cypher查询，最终提供自然语言答案，从而提升问答的智能化水平。

RustConf 2025 将探讨 Rust 在微软、人工智能和医疗等多个领域的应用，强调内存序的正确使用以避免数据竞争。同时，介绍了多个 Rust 项目和工具，如 beamterm 和 Utsuru，展示了 Rust 的广泛应用潜力。

本研究提出图基础模型（GFMs），旨在解决图数据在预训练和迁移学习中的挑战，并展示其在多种图任务中的应用潜力，为未来研究提供方向。

本研究批判性审视了图机器学习中的常见信念，如过度平滑、过度挤压、同质性与异质性二元论及长范围任务。通过反例，旨在澄清这些误解，促进深入讨论。

本研究提出符号图排序器（SGR），旨在提升大语言模型（LLMs）在会话搜索中的表现。通过将会话图转化为文本，增强了对图结构的理解，并通过自监督学习提升拓扑信息的捕捉能力。实验结果表明，该方法在基准数据集上表现优越，促进了传统搜索策略与现代LLMs的融合。

本研究提出了一种基于知识图谱的方法，解决大型语言模型在代码生成中的上下文准确性问题，显著提升了代码搜索与检索的质量，为开发更可靠的编码辅助工具提供了新方向。