Abstract: Influence maximization (IM) aims to select a seed set of users that maximizes the expected influence spread and is a fundamental problem in social network analysis. The dynamic and complex ...

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 ...

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 ...

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 ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

A Python program that tests network connectivity and latency to any host or IP address. The tool performs comprehensive network diagnostics and outputs results to a text file.

We constructed a backbone network based on commenter overlap and conducted a social network analysis (SNA) to examine the temporal dynamics. We further applied exponential random graph models (ERGMs) ...

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 ...