Abstract: Multivariate data contain an abundance of information and many techniques have been proposed to allow humans to navigate this information in an ordered fashion. For this work, we focus on ...

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 ...

This project serves as a complete portfolio demonstrating my journey from Python fundamentals to advanced data analytics techniques. It includes hands-on examples of data cleaning, exploration, ...

Abstract: Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data learning. In many applications, graph node attributes/features may contain various kinds of noises, such ...

The world of data visualization can leave you to choose between expensive software or difficult-to-learn software. lessR simplifies the process of obtaining professional-quality data visualizations at ...

Advanced data visualization and analytics have become central to enterprise IT strategies as organizations face rapid data growth from cloud services, software-as-a-service applications, edge devices, ...

Cybersecurity researchers have identified several malicious packages across npm, Python, and Ruby ecosystems that leverage Discord as a command-and-control (C2) channel to transmit stolen data to ...

Electrostatic interactions are fundamental to the structure, dynamics, and function of biomolecules, with broad applications in protein–ligand binding, enzymatic catalysis, and nucleic acid regulation ...

CrowdStrike Holdings Inc.’s Enterprise Graph solution is the company’s latest defense against cyberattacks fueled by agentic artificial intelligence. The innovation is part of the company’s new ...

Graphs and data visualizations are all around us—charting our steps, our election results, our favorite sports teams’ stats, and trends across our world. But too often, people glance at a graph ...