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Dissertations / Theses on the topic 'Graph Neural Networks (GNNs)'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Pappone, Francesco. "Graph neural networks: theory and applications." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23893/.

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Le reti neurali artificiali hanno visto, negli ultimi anni, una crescita vertiginosa nelle loro applicazioni e nelle architetture dei modelli impiegati. In questa tesi introduciamo le reti neurali su domini euclidei, in particolare mostrando l’importanza dell’equivarianza di traslazione nelle reti convoluzionali, e introduciamo, per analogia, un’estensione della convoluzione a dati strutturati come grafi. Inoltre presentiamo le architetture dei principali Graph Neural Network ed esponiamo, per ognuna delle tre architetture proposte (Spectral graph Convolutional Network, Graph Co

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Andersson, Mikael. "Gamma-ray racking using graph neural networks." Thesis, KTH, Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298610.

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While there are existing methods of gamma ray-track reconstruction in specialized detectors such as AGATA, including backtracking and clustering, it is naturally of interest to diversify the portfolio of available tools to provide us viable alternatives. In this study some possibilities found in the field of machine learning were investigated, more specifically within the field of graph neural networks. In this project there was attempt to reconstruct gamma tracks in a germanium solid using data simulated in Geant4. The data consists of photon energies below the pair production limit and so we

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Andersson, Mikael. "Gamma-ray tracking using graph neural networks." Thesis, KTH, Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298610.

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While there are existing methods of gamma ray-track reconstruction in specialized detectors such as AGATA, including backtracking and clustering, it is naturally of interest to diversify the portfolio of available tools to provide us viable alternatives. In this study some possibilities found in the field of machine learning were investigated, more specifically within the field of graph neural networks. In this project there was attempt to reconstruct gamma tracks in a germanium solid using data simulated in Geant4. The data consists of photon energies below the pair production limit and so we

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Gunnarsson, Robin, and Alexander Åkermark. "Approaching sustainable mobility utilizing graph neural networks." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45191.

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This report is done in collaboration with WirelessCar for the master of science thesis at Halmstad University. Many different parameters influence fuel consumption. The objective of the report is to evaluate if Graph neural networks are a practical model to perform fuel consumption prediction on areas. The model uses a partitioning of geographical locations of trip observations to capture their spatial information. The project also proposes a method to capture the non-stationary behavior of vehicles by defining a vehicle node as a separate entity. The model then captures their different featur

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Amanzadi, Amirhossein. "Predicting safe drug combinations with Graph Neural Networks (GNN)." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446691.

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Many people - especially during their elderly - consume multiple drugs for the treatment of complex or co-existing diseases. Identifying side effects caused by polypharmacy is crucial for reducing mortality and morbidity of the patients which will lead to improvement in their quality of life. Since there is immense space for possible drug combinations, it is infeasible to examine them entirely in the lab. In silico models can offer a convenient solution, however, due to the lack of a sufficient amount of homogenous data it is difficult to develop both reliable and scalable models in its abilit

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Liberatore, Lorenzo. "Introduction to geometric deep learning and graph neural networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25339/.

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This thesis proposes an introduction to the fundamental concepts of supervised deep learning. Starting from Rosemblatt's Perceptron we will discuss the architectures that, in recent years, have revolutioned the world of deep learning: graph neural networks, which led to the formulation of geometric deep learning. We will then give a simple example of graph neural network, discussing the code that composes it and then test our architecture on the MNISTSuperpixels dataset, which is a variation of the benchmark dataset MNIST.

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Nastorg, Matthieu. "Scalable GNN Solutions for CFD Simulations." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG020.

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La Dynamique des Fluides Numérique (CFD) joue un rôle essentiel dans la prédiction de divers phénomènes physiques, tels que le climat, l'aérodynamique ou la circulation sanguine. Au coeur de la CFD se trouvent les équations de Navier-Stokes régissant le mouvement des fluides. Cependant, résoudre ces équations à grande échelle reste fastidieux, en particulier lorsqu'il s'agit des équations de Navier-Stokes incompressibles, qui nécessitent la résolution intensive d'un problème de Poisson de Pression, garantissant la contrainte d'incompressibilité. De nos jours, les méthodes d'apprentissage profo

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Zheng, Xuebin. "Wavelet-based Graph Neural Networks." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/27989.

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This thesis focuses on spectral-based graph neural networks (GNNs). In Chapter 2, we use multiresolution Haar-like wavelets to design a framework of GNNs which equips with graph convolution and pooling strategies. The resulting model is called MathNet whose wavelet transform matrix is constructed with a coarse-grained chain. So our proposed MathNet not only enjoys the multiresolution analysis from the Haar-like wavelets but also leverages the clustering information of the graph data. Furthermore, we develop a novel multiscale representation system for graph data, called decimated framelets, w

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Olmucci, Poddubnyy Oleksandr. "Graph Neural Networks for Recommender Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25033/.

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In recent years, a new type of deep learning models, Graph Neural Networks (GNNs), have demonstrated to be a powerful learning paradigm when applied to problems that can be described via graph data, due to their natural ability to integrate representations across nodes that are connected via some topological structure. One of such domains is Recommendation Systems, the majority of whose data can be naturally represented via graphs. For example, typical item recommendation datasets can be represented via user-item bipartite graphs, social recommendation datasets by social networks, and so on. T

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Chen, Zhiqian. "Graph Neural Networks: Techniques and Applications." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99848.

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Effective information analysis generally boils down to the geometry of the data represented by a graph. Typical applications include social networks, transportation networks, the spread of epidemic disease, brain's neuronal networks, gene data on biological regulatory networks, telecommunication networks, knowledge graph, which are lying on the non-Euclidean graph domain. To describe the geometric structures, graph matrices such as adjacency matrix or graph Laplacian can be employed to reveal latent patterns. This thesis focuses on the theoretical analysis of graph neural networks and the deve

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Boszorád, Matej. "Segmentace obrazových dat pomocí grafových neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412987.

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This diploma thesis describes and implements the design of a graph neural network usedfor 2D segmentation of neural structure. The first chapter of the thesis briefly introduces the problem of segmentation. In this chapter, segmentation techniques are divided according to the principles of the methods they use. Each type of technique contains the essence of this category as well as a description of one representative. The second chapter of the diploma thesis explains graph neural networks (GNN for short). Here, the thesis divides graph neural networks in general and describes recurrent graph n

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Zhou, Bingxin. "Geometric Signal Processing with Graph Neural Networks." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28617.

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One of the most predominant techniques that have achieved phenomenal success in many modern applications is deep learning. The obsession with massive data analysis in image recognition, speech processing, and text understanding spawns remarkable advances in deep learning of diverse research areas. The alliance of deep learning technologies yields mighty graph neural networks (GNNs), an emerging type of deep neural networks that encodes internal structural relationships of inputs. The mainstream of GNNs finds an adequate numerical representation of graphs, which is vital to the prediction perfo

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Lachaud, Guillaume. "Extensions and Applications of Graph Neural Networks." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS434.

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Les graphes sont utilisés partout pour représenter les interactions, qu'elles soient physiques comme entre les atomes, les molécules ou les humains, ou plus abstraites comme les villes, les amitiés, les idées, etc. Parmi toutes les méthodes d'apprentissage automatique qui peuvent être utilisées, les dernières avancées en apprentissage profond font des réseaux de neurones de graphes la référence de l'apprentissage de représentation des graphes. Cette thèse se divise en deux parties. Dans un premier temps, nous faisons un état de l'art des fondations mathématiques des réseaux de neurones de grap

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Ferroni, Nicola. "Exact Combinatorial Optimization with Graph Convolutional Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17502/.

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Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed-integer linear programming, by imitation learning on a diversified variant of the strong branching expert rule. We encode states as bipartite graphs and parameterize the policy as a graph convolutional neural network. Experiments on a series of synthetic problems demonstrate that our approach produces policies that can improve upon expert-designed branching rules on large problems, and generalize to instances significantly lar

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Lombardi, Alessandro. "Multiple time series forecasting with Graph Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24729/.

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Time series forecasting aims to predict future values to support organizations making strategic decisions. This problem has been studied for decades due to its relevance in almost all industries and areas, ranging from financial data to product demand. Recently, modern solutions based on deep learning have gained popularity among academia and industry, mainly due to the necessity to automatize the forecasting of multiple time series and exploit external explanatory variables. Considering the recent successes of Graph Neural Networks (GNNs) in modelling graph data, this study extends previous w

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Kan, Jichao. "Visual-Text Translation with Deep Graph Neural Networks." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23759.

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Visual-text translation is to produce textual descriptions in natural languages from images and videos. In this thesis, we investigate two topics in the field: image captioning and continuous sign language recognition, by exploring structural representations of visual content. Image captioning is to generate text descriptions for a given image. Deep learning based methods have achieved impressive performance on this topic. However, the relations among objects in an image have not been fully explored. Thus, a topic-guided local-global graph neural network is proposed to extract graph propertie

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Donachy, Shaun. "Spiking Neural Networks: Neuron Models, Plasticity, and Graph Applications." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3984.

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Networks of spiking neurons can be used not only for brain modeling but also to solve graph problems. With the use of a computationally efficient Izhikevich neuron model combined with plasticity rules, the networks possess self-organizing characteristics. Two different time-based synaptic plasticity rules are used to adjust weights among nodes in a graph resulting in solutions to graph prob- lems such as finding the shortest path and clustering.

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Casellato, Claudio <1997&gt. "International Trade Modelling with Temporal Relational Graph Neural Networks." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/21414.

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Graph Neural Networks (GNN) are a powerful technique to model data on graph domains with neural networks. They are mainly used on static networks where nodes and edges do not change over time and only one type of edge is present between two nodes. To overcome this issue new models extended the GNN model to incorporate temporal data and relational data. The resulting model is defined as a Temporal Relational Graph Neural Networks (TRGNN). We use this novel technique to model the trade evolution of the International Trade Network (ITN) for different products. The nodes in the network represent t

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Liu, Wenzhuo. "Deep Graph Neural Networks for Numerical Simulation of PDEs." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG032.

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Les équations aux dérivées partielles (EDP) sont un outil essentiel de la simulation numérique pour modéliser des systèmes complexes. Cependant, la résolution de ces équations avec une grande précision nécessite généralement un coût de calcul élevé. Ces dernières années, les algorithmes d'apprentissage profond ont reçu un intérêt croissant pour l'apprentissage à partir d'exemples, et pourraient être utilisés comme substituts des méthodes d'analyse numérique, en appliquant directement les techniques d'apprentissage supervisé à des bases de données de solutions connues, car une fois le modèle ne

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Suárez-Varela, Macià José Rafael. "Enabling knowledge-defined networks : deep reinforcement learning, graph neural networks and network analytics." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669212.

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Significant breakthroughs in the last decade in the Machine Learning (ML) field have ushered in a new era of Artificial Intelligence (AI). Particularly, recent advances in Deep Learning (DL) have enabled to develop a new breed of modeling and optimization tools with a plethora of applications in different fields like natural language processing, or computer vision. In this context, the Knowledge-Defined Networking (KDN) paradigm highlights the lack of adoption of AI techniques in computer networks and – as a result – proposes a novel architecture that relies on Software-Defined Networking (

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Pasdeloup, Bastien. "Extending convolutional neural networks to irregular domains through graph inference." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0048/document.

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Tout d'abord, nous présentons des méthodes permettant d'inférer un graphe à partir de signaux, afin de modéliser le support des données à classifier. Ensuite, des translations préservant les voisinages des sommets sont identifiées sur le graphe inféré. Enfin, ces translations sont utilisées pour déplacer un noyau convolutif sur le graphe, afin dedéfinir un réseau de neurones convolutif adapté aux données d'entrée.Nous avons illustré notre méthodologie sur une base de données d'images. Sans utiliser de connaissances sur les signaux, nous avons pu inférer un graphe proche d'une grille. Les trans

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Ekström, Filip. "Graph neural networks for prediction of formation energies of crystals." Thesis, Linköpings universitet, Institutionen för fysik, kemi och biologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166995.

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Predicting formation energies of crystals is a common but computationally expensive task. In this work, it is therefore investigated how a neural network can be used as a tool for predicting formation energies with less computational cost compared to conventional methods. The investigated model shows promising results in predicting formation energies, reaching below a mean absolute error of 0.05 eV/atom with less than 4000 training datapoints. The model also shows great transferability, being able to reach below an MAE of 0.1 eV/atom with less than 100 training points when transferring from a

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Sangha, Manjot. "Scalability and interpretability of graph neural networks for small molecules." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/121639.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 95-98).<br>In this thesis I examine the use of graph neural networks for prediction tasks in chemistry with an emphasis on interpretable and scalable methods. I propose a novel kernel-inspired graph neural network architecture

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Friji, Hamdi. "Graph neural network-based intrusion detection for secure edge networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS030.

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Face à l'escalade de la complexité et à la fréquence des cyberattaques, cette thèse propose des approches innovantes pour la détection d'intrusion dans les réseaux, en exploitant les capacités avancées des réseaux de neurones en graphe (Graph Neural Networks, GNNs) et de nouvelles représentations sous forme de graphes. Nous commençons par une analyse critique des jeux de données et des représentations de réseaux actuels, en abordant des questions clés sur leur efficacité. Nous introduisons une nouvelle représentation des flux de communication sous forme de graphes, offrant une plus grande robu

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Quattromini, Michele. "Graph Neural Networks for fluid mechanics : data-assimilation and optimization." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST161.

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Cette thèse de doctorat explore l'application des réseaux de neurones en graphes (GNN) dans le domaine de la dynamique des fluides numérique (CFD), avec un accent particulier sur l'assimilation de données et l'optimisation. Le travail est structuré en trois parties principales: assimilation de données pour les équations de Navier-Stokes moyennées à la Reynolds (RANS) basée sur des modèles GNN; assimilation de données augmentée par les GNN avec des contraintes physiques imposées par la méthode adjointe; optimisation des systèmes fluides par des techniques d'apprentissage automatique (ML).Dans l

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Hoya, Tetsuya. "Graph theoretic methods for data partitioning." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286542.

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FUTIA, GIUSEPPE. "Neural Networks forBuilding Semantic Models and Knowledge Graphs." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850594.

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Krishnaswami, Sreedhar Bharathwaj. "Bayesian Optimization for Neural Architecture Search using Graph Kernels." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291219.

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Neural architecture search is a popular method for automating architecture design. Bayesian optimization is a widely used approach for hyper-parameter optimization and can estimate a function with limited samples. However, Bayesian optimization methods are not preferred for architecture search as it expects vector inputs while graphs are high dimensional data. This thesis presents a Bayesian approach with Gaussian priors that use graph kernels specifically targeted to work in the higherdimensional graph space. We implemented three different graph kernels and show that on the NAS-Bench-101 data

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Harris, William H. (William Hunt). "Machine learning transferable physics-based force fields using graph convolutional neural networks." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128979.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 22-24).<br>Molecular dynamics and Monte Carlo methods allow the properties of a system to be determined from its potential energy surface (PES). In the domain of crystalline materials, the PES is needed for electronic structure calculations, critical for modeling semiconductors, optical, and energy-storage materials. While first principles techniques can be used to obtain the PES to high accur

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Wu, Jindong. "Pooling strategies for graph convolution neural networks and their effect on classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288953.

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With the development of graph neural networks, this novel neural network has been applied in a broader and broader range of fields. One of the thorny problems researchers face in this field is selecting suitable pooling methods for a specific research task from various existing pooling methods. In this work, based on the existing mainstream graph pooling methods, we develop a benchmark neural network framework that can be used to compare these different graph pooling methods. By using the framework, we compare four mainstream graph pooling methods and explore their characteristics. Furthermore

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Ajamlou, Kevin, and Max Sonebäck. "Multimodal Convolutional Graph Neural Networks for Information Extraction from Visually Rich Documents." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445457.

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Monotonous and repetitive tasks consume a lot of time and resources in businesses today and the incentive to fully or partially automate said tasks, in order to relieve office workers and increase productivity in the industry, is therefore high. One such task is to process and extract information from Visually Rich Documents (VRD:s), e.g., documents where the visual attributes contain important information about the contents of the document. A lot of recent studies have focused on information extraction from invoices, where graph based convolutional nerual networks have shown a lot of promise

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Ebrahimi, Javid. "Robustness of Neural Networks for Discrete Input: An Adversarial Perspective." Thesis, University of Oregon, 2019. http://hdl.handle.net/1794/24535.

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In the past few years, evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models. Literature on adversarial examples for neural nets has largely focused on image data, which are represented as points in continuous space. However, a vast proportion of machine learning models operate on discrete input, and thus demand a similar rigor in understanding their vulnerabilities and robustness. We study robustness of neural network architectures for textual and graph inputs, through the lens of adversarial input perturbations.

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Yuan, Xiao. "Graph neural networks for spatial gene expression analysis of the developing human heart." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-427330.

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Single-cell RNA sequencing and in situ sequencing were combined in a recent study of the developing human heart to explore the transcriptional landscape at three developmental stages. However, the method used in the study to create the spatial cellular maps has some limitations. It relies on image segmentation of the nuclei and cell types defined in advance by single-cell sequencing. In this study, we applied a new unsupervised approach based on graph neural networks on the in situ sequencing data of the human heart to find spatial gene expression patterns and detect novel cell and sub-cell ty

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Scotti, Andrea. "Graph Neural Networks and Learned Approximate Message Passing Algorithms for Massive MIMO Detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284500.

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Massive multiple-input and multiple-output (MIMO) is a method to improvethe performance of wireless communication systems by having a large numberof antennas at both the transmitter and the receiver. In the fifth-generation(5G) mobile communication system, Massive MIMO is a key technology toface the increasing number of mobile users and satisfy user demands. At thesame time, recovering the transmitted information in a massive MIMO uplinkreceiver requires more computational complexity when the number of transmittersincreases. Indeed, the optimal maximum likelihood (ML) detector hasa complexity

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Bereczki, Márk. "Graph Neural Networks for Article Recommendation based on Implicit User Feedback and Content." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300092.

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Recommender systems are widely used in websites and applications to help users find relevant content based on their interests. Graph neural networks achieved state- of-the- art results in the field of recommender systems, working on data represented in the form of a graph. However, most graph- based solutions hold challenges regarding computational complexity or the ability to generalize to new users. Therefore, we propose a novel graph- based recommender system, by modifying Simple Graph Convolution, an approach for efficient graph node classification, and add the capability of generalizing t

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Larsson, Sofia. "A Study of the Loss Landscape and Metastability in Graph Convolutional Neural Networks." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273622.

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Many novel graph neural network models have reported an impressive performance on benchmark dataset, but the theory behind these networks is still being developed. In this thesis, we study the trajectory of Gradient descent (GD) and Stochastic gradient descent (SGD) in the loss landscape of Graph neural networks by replicating Xing et al. [1] study for feed-forward networks. Furthermore, we empirically examine if the training process could be accelerated by an optimization algorithm inspired from Stochastic gradient Langevin dynamics and what effect the topology of the graph has on the converg

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Ljubenkov, Davor. "Optimizing Bike Sharing System Flows using Graph Mining, Convolutional and Recurrent Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257783.

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A Bicycle-sharing system (BSS) is a popular service scheme deployed in cities of different sizes around the world. Although docked bike systems are its most popular model used, it still experiences a number of weaknesses that could be optimized by investigating bike sharing network properties and evolution of obtained patterns.Efficiently keeping bicycle-sharing system as balanced as possible is the main problem and thus, predicting or minimizing the manual transportation of bikes across the city is the prime objective in order to save logistic costs for operating companies.The purpose of this

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Reginbald, Ivarsson Jón. "Scalable System-Wide Traffic Flow Predictions Using Graph Partitioning and Recurrent Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254896.

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Traffic flow predictions are an important part of an Intelligent Transportation System as the ability to forecast accurately the traffic conditions in a transportation system allows for proactive rather than reactive traffic control. Providing accurate real-time traffic predictions is a challenging problem because of the nonlinear and stochastic features of traffic flow. An increasingly widespread deployment of traffic sensors in a growing transportation system produces greater volume of traffic flow data. This results in problems concerning fast, reliable and scalable traffic predictions.The

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Norrsjö, Viktor. "Prediction of compound solubility in Dimethyl sulfoxide using machinelearning methods including graph neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283113.

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In drug discovery, compounds that are insoluble in Dimethyl sulfoxide (DMSO) are not wanted and can be disregarded. To avoid wasting time and resources pharmaceutical companies are trying to predict compound solubility before selecting compounds for further research. Compound solubility is hard to predict with confidence and this project focus on prediction using machine learning methods. The used dataset consists of almost 12 thousand compounds label soluble or insoluble and is very label biased towards soluble compounds. Different ways of representing compounds are tested with the four machi

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Kothari, Bhavin Chandrakant. "Structural optimisation of artificial neural networks by the genetic algorithm using a new encoding scheme." Thesis, Brunel University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389263.

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Delissen, Johan. "Graph Based Machine Learning approaches and Clustering in a Customer Relationship Management Setting." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81892.

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This master thesis investigates the utilisation of various graph based machine learning models for solving a customer segmentation problem, a task coupled to Customer Relationship Management, where the objective is to divide customers into different groups based on similar attributes. More specifically a customer segmentation problem is solved via an unsupervised machine learning technique named clustering, using the k-means clustering algorithm. Three different representations of customers as a vector of attributes are created and then utilised by the k-means algorithm to divide users into di

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Yildiz, Ali. "Resource-aware Load Balancing System With Artificial Neural Networks." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607613/index.pdf.

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As the distributed systems becomes popular, efficient load balancing systems taking better decisions must be designed. The most important reasons that necessitate load balancing in a distributed system are the heterogeneous hosts having different com- puting powers, external loads and the tasks running on different hosts but communi- cating with each other. In this thesis, a load balancing approach, called RALBANN, developed using graph partitioning and artificial neural networks (ANNs) is de- scribed. The aim of RALBANN is to integrate the successful load balancing

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West, Geoffrey Michael Jonathan. "Template based prediction : using neural networks and graph templates to predict nuclear magnetic resonance shifts." Thesis, Staffordshire University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362057.

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Harb, Ihab A. "An approach to pattern recognition of multifont printed alphabet using conceptual graph theory and neural networks." PDXScholar, 1989. https://pdxscholar.library.pdx.edu/open_access_etds/3923.

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This thesis describes an approach for accomplishing a pattern recognition task using conceptual graph theory and neural networks (NNs). The set of patterns to be recognized are the capital letters of six different fonts of the English alphabet, plus two shifted and six rotated versions of each. The letters are represented to the neural network on a 16x16 input grid (256 "sensor lines"). A standard classification encoding for such patterns is to use a 26-bit vector (26 lines at the NN's output), one bit corresponding to each letter. Experiments with such an encoding yielded results with poor ge

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Sun, Qing. "Greedy Inference Algorithms for Structured and Neural Models." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/81860.

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A number of problems in Computer Vision, Natural Language Processing, and Machine Learning produce structured outputs in high-dimensional space, which makes searching for the global optimal solution extremely expensive. Thus, greedy algorithms, making trade-offs between precision and efficiency, are widely used. %Unfortunately, they in general lack theoretical guarantees. In this thesis, we prove that greedy algorithms are effective and efficient to search for multiple top-scoring hypotheses from structured (neural) models: 1) Entropy estimation. We aim to find deterministic samples that are

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Weller, Tobias [Verfasser], and Y. [Akademischer Betreuer] Sure-Vetter. "Learning Latent Features using Stochastic Neural Networks on Graph Structured Data / Tobias Weller ; Betreuer: Y. Sure-Vetter." Karlsruhe : KIT-Bibliothek, 2021. http://d-nb.info/1230475656/34.

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Gullstrand, Mattias, and Stefan Maraš. "Using Graph Neural Networks for Track Classification and Time Determination of Primary Vertices in the ATLAS Experiment." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288505.

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Starting in 2027, the high-luminosity Large Hadron Collider (HL-LHC) will begin operation and allow higher-precision measurements and searches for new physics processes between elementary particles. One central problem that arises in the ATLAS detector when reconstructing event information is to separate the rare and interesting hard scatter (HS) interactions from uninteresting pileup (PU) interactions in a spatially compact environment. This problem becomes even harder to solve at higher luminosities. This project relies on leveraging the time dimension and determining a time of the HS intera

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Kilinc, Ismail Ozsel. "Graph-based Latent Embedding, Annotation and Representation Learning in Neural Networks for Semi-supervised and Unsupervised Settings." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7415.

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Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and uns

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Papakis, Ioannis. "A Graph Convolutional Neural Network Based Approach for Object Tracking Using Augmented Detections With Optical Flow." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103372.

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This thesis presents a novel method for online Multi-Object Tracking (MOT) using Graph Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature matching for object association. The Graph based approach incorporates both appearance and geometry of objects at past frames as well as the current frame into the task of feature learning. This new paradigm enables the network to leverage the "contextual" information of the geometry of objects and allows us to model the interactions among the features of multiple objects. Another central innovation of the proposed framework

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Belghaddar, Yassine. "Data fusion for urban network mapping : application to wastewater networks." Electronic Thesis or Diss., Université de Montpellier (2022-....), 2022. http://www.theses.fr/2022UMONG092.

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Nombreuses sont les raisons qui rendent la maitrise des données des réseaux souterrains essentielle~: réduire le coût des réparations et des interventions, lancer des simulations hydrauliques, préserver l'environnement etc. Les données disponibles relatives à ces réseaux et plus spécifiquement ceux d'assainissement sont diverses en terme de types (textes, images, SIG, etc.) et de formats (analogique, numérique). De plus, ces données émanant de sources multiples sont généralement incomplètes, imprécises, incertaines et parfois contradictoires. De ce fait, dans le but d'extraire l'information pe

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