In this tutorial, we build a fully offline Graphify workflow that turns a realistic multi-module Python application into a knowledge graph. We start by installing Graphify and supporting graph ...
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 ...
The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph neural networks (GNNs) have emerged as a powerful tool in predicting molecular ...
Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
Abstract: In this letter, we propose a meta-learning-based fast adversarial training method to address the vulnerability of graph neural network (GNN) based resource allocation method to adversarial ...
Abstract: A Graph Neural Network (GNN) for learning beamforming in Pinching Antenna Systems (PASS) is proposed, which is called GPASS. The GPASS is with a staged architecture, where the positions of ...
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