Essays about: "node graph"

Showing result 6 - 10 of 94 essays containing the words node graph.

  1. 6. An experimental analysis of Link Prediction methods over Microservices Knowledge Graphs

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

    Author : Gianluca Ruberto; [2023]
    Keywords : Knowledge Graphs; Link Prediction; Machine Learning; Microservice Tracing; Kunskapsdiagram; länkförutsägelse; maskininlärning; mikroservicespårning;

    Abstract : Graphs are a powerful way to represent data. They can be seen as a collection of objects (nodes) and the relationships between them (edges or links). The power of this structure has its intrinsic value in the relationship between data points that can even provide more information than the data properties. READ MORE

  3. 7. 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. 8. RNN-based Graph Neural Network for Credit Load Application leveraging Rejected Customer Cases

    University essay from Högskolan i Halmstad/Akademin för informationsteknologi

    Author : Oskar Nilsson; Benjamin Lilje; [2023]
    Keywords : Machine Learning; Deep Learning; Reject Inference; GNN; GCN; Graph Neural Networks; RNN; Recursive Neural Network; LSTM; Semi-Supervised Learning; Encoding; Decoding; Feature Elimination;

    Abstract : Machine learning plays a vital role in preventing financial losses within the banking industry, and still, a lot of state of the art and industry-standard approaches within the field neglect rejected customer information and the potential information that they hold to detect similar risk behavior.This thesis explores the possibility of including this information during training and utilizing transactional history through an LSTM to improve the detection of defaults. READ MORE

  5. 9. Time synchronization error detection in a radio access network

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

    Author : Moulika Madana; [2023]
    Keywords : GNSS - Global Navigation Satellite System; OAS - Over-the air-synchronization; PRTC - primary reference time clock; PTP - precision time protocol; Gauss Jordan elimination; GNN- Graph Neural Network; GNSS -Globalt navigationssatellitsystem; OAS - Över-the-air tidssynkronisering; PRTC - Primär referenstidklocka; PTP - Precisionstidprotokoll; Gauss Jordan eliminering; GNN- Graf neurala nätverk;

    Abstract : Time synchronization is a process of ensuring all the time difference between the clocks of network components(like base stations, boundary clocks, grandmasters, etc.) in the mobile network is zero or negligible. It is one of the important factors responsible for ensuring effective communication between two user-equipments in a mobile network. READ MORE

  6. 10. 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