See how to query documents using natural language, LLMs, and R—including dplyr-like filtering on metadata. Plus, learn how to ...

Google's new Graph Foundation Model delivers up to 40 times greater precision and has been tested at scale on spam detection.

Investors need a new framework for evaluating opportunities in the emerging Intelligence Economy ("Intellinomics"). Semantic ...

Build AI into your enterprise content and knowledge management platform with 5 APIs that help you base your AI on enterprise ...

The bottom line is that “graph databases are a type of NoSQL database that stores data as a network of interconnected nodes and edges,” Nadkarni explained. “They manage complex relationships between ...

Neo4j Aura Graph Analytics comes with more than 65 ready-to-use graph algorithms and is optimized for high-performance AI applications, with support for parallel workflows ensuring any app can ...

The Graph introduces GRC-20, a Web3 data standard redefining how decentralized applications structure and share data.

Empowered by their remarkable advantages, graph neural networks (GNN) serve as potent tools for embedding graph-structured data and finding applications across various domains. Particularly, a ...

As real-world applications of graph-structured data expand, there is an increasing demand for models that can effectively generalize across different graph domains and handle the inherent diversity ...

Overview Graph pooling is an essential component of GNNs for graph-level representations. The goal of graph pooling is to learn a graph representation that captures topology, node features, and other ...