Abstract: Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on ...

Graphs are everywhere. From technology to finance, they often model valuable information such as people, networks, biological pathways and more. Often, scientists and technologists need to come up ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

LangGraph is a powerful framework by LangChain designed for creating stateful, multi-actor applications with LLMs. It provides the structure and tools needed to build sophisticated AI agents through a ...

Rajiv Shesh is the Chief Revenue Officer at HCLSoftware where he leads revenue growth & customer advocacy for Products & Platforms division. What’s really powering AI? High-quality data—foundational ...

For decades, enterprise data infrastructure focused on answering the question: “What happened in our business?” Business intelligence tools, data warehouses, and pipelines were built to surface ...

What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for ...

Neo4j Inc. today announced a new serverless offering that dramatically simplifies the deployment of its graph database offering, making it easier to use with artificial intelligence applications. Most ...

Structure content for AI search so it’s easy for LLMs to cite. Use clarity, formatting, and hierarchy to improve your visibility in AI results. In the SEO world, when we talk about how to structure ...

If you’re like me, you’ve heard plenty of talk about entity SEO and knowledge graphs over the past year. But when it comes to implementation, it’s not always clear which components are worth the ...

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific ...