Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 81 for “"GNN"”.
-
Sampling Methods for Fast and Versatile GNN Training
Graph neural networks (GNNs) have become a commonly used class of machine learning models that achieve state-of-the-art performance in various applications. A prevalent and effective approach for applying GNNs on large datasets involves mini-batch training with sampled neighborhoods. Numerous …
-
GNN-Enhanced Hierarchical Federated Learning in Device-to-Device Networks
… thesis investigates a Graph Neural Networks (GNN)-enhanced hierarchical FL architecture in D2D networks, aiming to achieve efficient, adaptive, and scalable federated model training across distributed devices. To begin with, this thesis proposes an asynchronous hierarchical clustered FL method …
-
Designing scalable large-scale storage-based GNN framework by efficiently leveraging heterogeneous hardware resources
The student, Jeongmin Park, submitted this Dissertation for approval on 2024-12-03 at 22:05.
-
Inference in Ising models by graph neural networks with structural features
… Recently Graphical Neural Networks (GNNs) are shown to outperform BP on small-scale loopy graphs. GNN computes a more general function on each node using neural networks, and learns the exact distribution of small loop-free and loopy graphs. As BP is exact on loop-free graphs and …
-
Structure-based learning via graph neural networks for multi-group multicast beamforming
… problem by developing a graph neural network (GNN) model and explore the available optimal multicast beamforming structure to speed up the training process and improve performance. A scalable GNN-based beamformer architecture critically depends on the design of the hidden layers. By exploring …
-
On Counting Substructures with Graph Neural Networks
… representation, most Graph Neural Networks (GNNs) follow two steps: first, each graph is decomposed into a number of subgraphs (which we call the recursion step), and then the collection of subgraphs is encoded by several iterative pooling steps. While recently proposed higher-order networks …
-
Machine Learning Driven Source Identification, State Estimation and Sensor Optimization in Water Systems
… first attempts to apply Graph Neural Networks (GNN) to estimate water quality parameters at unmonitored junctions. This was achieved by developing two GNN models. The first is a Static Prediction GNN (SP-GNN) model, which provides accurate state estimation for fixed sensor configurations. The …
-
Logic, Learning, and Explanation: Theoretical and Applied Perspectives on Machine Reasoning
… logical expressivity of Graph Neural Networks (GNNs), interpretability methods for GNNs, and the use of Large Language Models (LLMs) in legal reasoning. The first part develops a novel Ehrenfeucht-Fraïssé game tailored to counting logic with a bounded number of variables, which characterizes …
-
Deep learning for distributed circuit design
… We propose two models, Circuit-Net and Circuit-GNN. Circuit-Net is a complex-valued residual network that, once trained, can accurately generate simulation results for a specific type of circuit. Circuit-GNN is an extension of Circuit-Net, which exploits the flexibility of Graph Neural Network …
-
Optimizing Graph Neural Network Training on Large Graphs in A Distributed Setting
Graph neural networks (GNNs) are an important class of methods for leveraging the information present in graph structures to perform various learning tasks. Distributed GNNs can improve the performance of GNN execution by dividing computation among multiple machines and scale to large graphs by …
-
Towards an Efficient Network Intrusion Detection System for IoT Networks Leveraging Graph Neural Networks
… flows independently. Graph Neural Networks (GNNs) have emerged as a promising alternative having the ability to capture the underlying network topology. However, existing approaches focus solely on either node or edge features, limiting their capacity to fully understand the complexities of …
-
Efficient Systems for Large-Scale Graph Representation Learning
… to graphstructured data. Graph neural networks (GNNs), which integrate the power of deep learning with graph structures, have emerged as the leading methods in this field, delivering superior performance across diverse graph related tasks. However, training graph neural networks on large-scale …
-
From GNNs to sparse transformers: graph-based architectures for multi-hop question answering
… [7] have surpassed Graph Neural Networks (GNNs) as the state-of-the-art architecture for MHQA. Noting that the Transformer [4] is a particular message passing GNN, in this work we perform an architectural analysis and evaluation to investigate why the Transformer outperforms other GNNs on …
-
GraphDHT: Scaling Graph Neural Networks' Distributed Training on Edge Devices on a Peer-to-Peer Distributed Hash Table Network
… strategy for distributed Graph Neural Network (GNN) training, leveraging a peer-to-peer network of heterogeneous edge devices interconnected through a Distributed Hash Table (DHT). As GNNs become increasingly vital in analyzing graph-structured data across various domains, they pose unique …
-
Graph Neural Networks for Multi-Agent Learning
… as special cases under graph neural networks (GNNs), a framework for operating over any graph-structured data. Taking advantage of relational inductive biases, GNNs use local filters to learn functions that generalise over high-dimensional data. They are particularly useful in the context of …
-
Machine learning techniques for calorimeter cluster calibration of the CMS particle flow algorithm.
… Decision Trees (BDT) and Graph Neural Networks (GNN), are employed to calibrate PF energy clusters, improving both the response and the resolution of the measured energy. This thesis applies BDT to calibrate PF ECAL clusters, while GNN is tested for hadronic cluster calibration.
-
Machine learning techniques for calorimeter cluster calibration of the CMS particle flow algorithm.
… Decision Trees (BDT) and Graph Neural Networks (GNN), are employed to calibrate PF energy clusters, improving both the response and the resolution of the measured energy. This thesis applies BDT to calibrate PF ECAL clusters, while GNN is tested for hadronic cluster calibration.
-
Belief propagation on factor graph neural networks
… this thesis, we propose a Graph Neural Networks (GNN) approach for belief propagation based on message passing mechanisms. In the proposed approach, representations and other functions are learned by the GNN. We apply this approach to the inference of loopy factor graphs. Furthermore, we show that …
-
Balancing Memory Efficiency and Accuracy in Spectral-Based Graph Transformers
… caught up to mainstream Graph Neural Network (GNN) variants. A major limitation is the large O(𝑛2) memory consumption of graph transformers, where 𝑛 is the number of nodes. Therefore, we develop a memory-efficient graph transformer for node classification, capable of handling graphs with …
Page 1 of 5