Abstract: Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity and can only handle graphs with at most thousands of nodes. To this ...

Abstract: Node importance estimation involves assigning a global importance score to each node in a graph, pivotal to various subsequent tasks, including recommendation, network dismantling, etc.

This groundbreaking Software Development Kit offers a new approach to digital twin creation, combining an innovative in-memory, graph-based architecture with native Python integration. TwinGraph© SDK ...

In this tutorial, we guide you through the development of an advanced Graph Agent framework, powered by the Google Gemini API. Our goal is to build intelligent, multi-step agents that execute tasks ...

Currently, the Graph pattern in Strands Agents only supports Agent or MultiAgentBase instances as nodes. This limitation makes it difficult to implement deterministic ...

Building a chatbot can feel like an overwhelming task, especially when you’re juggling multiple tools and trying to ensure everything works seamlessly. If you’ve ever found yourself stuck between ...

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...

Then open JupyterLab, VS Code, or your favorite editor to start developing. Changes made in js/ will be reflected in the notebook.