Recently, researchers from the Pasqal team have made significant progress in the field of graph analysis. They proposed a new method that effectively enhances the accuracy and efficiency of graph ...

This is our PyTorch implementation of EPAGCL: Yanchen Xu +, Siqi Huang +, Hongyuan Zhang *, and Xuelong Li *, "Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?", ...

Abstract: Detecting fraud in multi-relational graphs is a significant difficulty due to the complex nature of fraudulent activities and the inadequacies of conventional Graph Neural Networks (GNNs) in ...

Abstract: Centrality measurement and network centralization are crucial for understanding and managing graph networks. This paper introduces a novel method for network centralization, termed the ...

The latest information from the National Intellectual Property Administration shows that Shengdi Xingtou Information ...

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 ...

Understand the merits of large language models vs. small language models, and why knowledge graphs are the missing piece in ...

This is the official companion repository for the book The Complete LangGraph Blueprint: Build 50+ AI Agents for Business Success. The repository provides source code, practical examples, and ...

Google DeepMind and Intrinsic developed AI that uses graph neural networks and reinforcement learning to automate multi-robot ...

Enterprise search startup Glean has attracted customers and venture capital thanks to an AI search tool that lets employees ...

A next-generation graph-relational database (DB) system has been developed in South Korea. If this system is applied in ...