Like many of us, [Tim]’s seen online videos of circuit sculptures containing illuminated LED filaments. Unlike most of us, however, he went a step further by using graph theory to design glowing ...

Abstract: Graph Neural Networks (GNNs) have gained popularity as an efficient choice for learning on graph-structured data. However, most methods are node or graph-centered, often overlooking valuable ...

Abstract: Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e., graph ...

This repository contains the implementation of EATSA-GNN, a novel approach to improve graph neural networks through edge-aware and two-stage attention mechanisms. The model leverages teacher-student ...

This project is a comprehensive guide to building AI agents using LangGraph, an open-source framework for graph-based AI workflows. It covers fundamental programming concepts, LangGraph principles, ...