Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...

Graph Neural Networks (GNNs) have emerged as a powerful class of models for learning from graph-structured data, capturing complex relational patterns across nodes and edges. However, their inherent ...

Graph data, e.g., social and biological networks, financial transactions, knowledge graphs, and transportation systems are pervasive in the natural world, where nodes are entities with features, and ...

Debate and discussion around data management, analytics, BI and information governance. In a guest blogpost, Neo4j’s Alyson Welch explains why Large Language Model AI systems can’t move beyond ...

The so-called "black-box" aspect of AI, usually referred to as the explainability problem, or X(AI) for short, arose slowly over the past few years. Still, with the rapid development in AI, it is now ...