Essays about: "Graph neural networks GNNs"

Showing result 1 - 5 of 12 essays containing the words Graph neural networks GNNs.

1. 1. Intersecting Graph Representation Learning and Cell Profiling : A Novel Approach to Analyzing Complex Biomedical Data

   University essay from Uppsala universitet/Institutionen för farmaceutisk biovetenskap

   Author : Nima Chamyani; [2023]
   Keywords : Graph representation learning; Cell profiling; Biological systems; Network medicine; Graphs; Machine learning techniques; Graph neural networks GNNs ; Protein-Compound-Pathway interactions; Biomarkers; Drug discovery;

   Abstract : In recent biomedical research, graph representation learning and cell profiling techniques have emerged as transformative tools for analyzing high-dimensional biological data. The integration of these methods, as investigated in this study, has facilitated an enhanced understanding of complex biological systems, consequently improving drug discovery. READ MORE

3. 2. Link Prediction Using Learnable Topology Augmentation

   University essay from KTH/Matematik (Avd.)

   Author : Tori Leatherman; [2023]
   Keywords : Network Analysis; Inductive Link Prediction; Learnable Augmentation; Graph Neural Networks; Multilayer Perceptrons; Nätverksanalys; Induktiv Länkförutsägelse; Inlärningsbar Förstärkning; Grafiska Neurala Nätverk; Flerskiktsperceptroner;

   Abstract : Link prediction is a crucial task in many downstream applications of graph machine learning. Graph Neural Networks (GNNs) are a prominent approach for transductive link prediction, where the aim is to predict missing links or connections only within the existing nodes of a given graph. READ MORE

4. 3. Estimation of Voltage Drop in Power Circuits using Machine Learning Algorithms : Investigating potential applications of machine learning methods in power circuits design

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Dimitrios Koutlis; [2023]
   Keywords : Voltage drop estimation; Application-specific Integrated Circuits ASICs ; Machine learning algorithms; XGBoost; Convolutional Neural Networks; Graph Neural Networks; Power circuit optimization; Uppskattning av spänningsfall; applikationsspecifika integrerade kretsar ASIC ; maskininlärningsalgoritmer; XGBoost; konvolutionella neurala nätverk; optimering av strömkretsar;

   Abstract : Accurate estimation of voltage drop (IR drop), in Application-Specific Integrated Circuits (ASICs) is a critical challenge, which impacts their performance and power consumption. As technology advances and die sizes shrink, predicting IR drop fast and accurate becomes increasingly challenging. READ MORE

5. 4. The Applicability and Scalability of Graph Neural Networks on Combinatorial Optimization

   University essay from KTH/Matematik (Avd.)

   Author : Peder Hårderup; [2023]
   Keywords : applied mathematics; combinatorial optimization; machine learning; graph neural networks; scalability; tillämpad matematik; kombinatorisk optimering; maskininlärning; grafiska neurala nätverk; skalbarhet;

   Abstract : This master's thesis investigates the application of Graph Neural Networks (GNNs) to address scalability challenges in combinatorial optimization, with a primary focus on the minimum Total Dominating set Problem (TDP) and additionally the related Carrier Scheduling Problem (CSP) in networks of Internet of Things. The research identifies the NP-hard nature of these problems as a fundamental challenge and addresses how to improve predictions on input graphs of sizes much larger than seen during training phase. READ MORE

6. 5. Graph Attention Networks for Link Prediction in Semantic Word Grouping

   University essay from Uppsala universitet/Avdelningen för beräkningsvetenskap

   Author : Anton Gollbo; [2023]
   Keywords : ;

   Abstract : Manually extracting relevant information from extensive amounts of data can betime-consuming and labour-intensive. Automating this process can allow for a shift of focus toward analysis and utilization of the extracted information, rather than allocating time and resources to data collection and preparation. READ MORE