Abstract: To address the issue of limited topological generalization in Graph Attention Networks (GAT) due to the fixed hop range, this paper proposes a Random-K-Hop Graph Attention Network (RKGAT) to ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Abstract: Vision Graph Neural Network (ViG) is the first graph neural network model capable of directly processing image data. The community primarily focuses on the model structures to improve ViG’s ...

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

In nature, random fiber networks such as some of the tissues in the human body, are strong and tough with the ability to hold together but also stretch a lot before they fail. Studying this structural ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

Imagine standing atop a mountain, gazing at the vast landscape below, trying to make sense of the world around you. For centuries, explorers relied on such vantage points to map their surroundings.

This is a simple network scanner used to scan any range of IP Address to get their MAC Address. The code is written completely in Python (currently, python v3).