Essays about: "Temporal Graph Neural Networks"
Showing result 1 - 5 of 9 essays containing the words Temporal Graph Neural Networks.
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1. Anomaly Detection in the EtherCAT Network of a Power Station : Improving a Graph Convolutional Neural Network Framework
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In this thesis, an anomaly detection framework is assessed and fine-tuned to detect and explain anomalies in a power station, where EtherCAT, an Industrial Control System, is employed for monitoring. The chosen framework is based on a previously published Graph Neural Network (GNN) model, utilizing attention mechanisms to capture complex relationships between diverse measurements within the EtherCAT system. READ MORE
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2. Cyber Threat Detection using Machine Learning on Graphs : Continuous-Time Temporal Graph Learning on Provenance Graphs
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Cyber attacks are ubiquitous and increasingly prevalent in industry, society, and governmental departments. They affect the economy, politics, and individuals. READ MORE
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3. Graph Neural Networks for Events Detection in Football
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Tracab’s optical tracking system allows to track the 2-dimensional trajectories of players and ball during a football game. Using this data it is possible to train machine learning models to identify events that happen during the match. READ MORE
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4. Software Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The increasing complexity of telecom networks, induced by the recent development of 5G, is a challenge for detecting faults in the telecom network. In addition to the structural complexity of telecommunication systems, data accessibility has become an issue both in terms of privacy and access cost. READ MORE
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5. Design and Implementation of Cellular Network Hotspot Forecast Using Graph Convolutional Neural Networks
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : This project proposes a type of (Recurrent) Graph Neural Network (RGNN) to predict future hotspots in cellular network data. Current state-of-the-art algorithms process each antenna (celldata )’s in isolation, ignoring the performance of nearby cells and cell locations. READ MORE