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.
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Showing 1 to 18 of 18 for “"Node classification"”.
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Node Classification on Relational Graphs Using Deep-RGCNs
… in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data characteristic of realistic knowledge graphs for node property …
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Node classification with extremely few labels and applications to social networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms
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Benchmarking Graph Transformers Toward Scalability for Large Graphs
… that performs comparably to GraphGPS on the node classification task on the Cora and CiteSeer datasets. Compared to the modified version of SAN that we started with, our architecture is faster to train and evaluate, and also obtains higher node classification accuracies on the Cora and …
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Balancing Memory Efficiency and Accuracy in Spectral-Based Graph Transformers
… 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 thousands of nodes while maintaining accuracy. Specifically, we reduce the memory use in the attention mechanism and add a …
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Real-time analytics for complex structure data
… with dynamic changes, such as new instances, new nodes and edges, and modifications to the node content. Different from traditional data, which are represented as feature vectors, data with complex relationships are often represented as graphs to denote the content of the data entries and their …
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Locating People of Interest in Social Networks
… individuals (key players, most influential nodes) in a network. We consider the same problem in this dissertation, with the constraint that the individuals we are interested in identifying (People of Interest) are not necessarily the most important nodes in terms of the network structure. We …
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FAST LEARNING ON GRAPHS
We carry out a systematic study of classification problems on networked data, presenting novel techniques with good performance both in theory and in practice. We assess the power of node classification based on class-linkage information only. In particular, we propose four new algorithms that …
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Online and active learning of big networks: theory and algorithms
… On the other hand, the labels of the nodes in big networks are scarce. It is urgent to optimize the process by which the labels are collected, because it is unrealistic to get labels of every node. The objective of my research is to develop algorithms for big network analytics, which …
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Graph feature engineering and coordinate-based learning for transferable and energy-efficient artificial intelligence
… descriptors into a unified representation for node classification. The second contribution is the Topology Coordinate-Driven Random Forests (TC-DRF) framework, which combines anchor-based topology coordinates with Random Forest classifiers for graph-level learning and cross-dataset transfer. …
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Multimodal learning and language models for enhanced knowledge representations
… pretrained language models, achieving superior node classification performance on text-attributed hypergraphs. The second contribution tackles dense retrieval in environments with limited labeled data by developing a novel weakly supervised semantic distillation framework. Leveraging the rich …
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Sampling Methods for Fast and Versatile GNN Training
… system for fast training of GNNs with node-wise sampling. In experiments with 4 GNN models which use layer-wise and subgraph sampling, FlexSample achieves up to 1.3× speed-up for end-to-end training over PyTorch Geometric with the same sampling code. Furthermore, FlexSample extends …
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Intrusion Detection using Bit Timing Characteristics for CAN Bus
… Electronic Control Units (ECUs), also called nodes on the CAN bus. Each ECU on the CAN bus is a microcontroller that sends a unique identifier used for node identification. It is possible to spoof node A by sending the same identifier through node B and thereby control node A. Thus, a hacker …
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Prediction and modelling of complex social networks and their evolution.
… relational components (links) between instances (nodes) of the network. These links and nodes induce intricate local and global patterns, defining the topology of a network. The topology is ever evolving, determining the dynamics of such a networked system. The work presented in this thesis starts …
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Learning representations for information mining from text corpora with applications to cyber threat intelligence
… and specific cyber threats are classified using classification models based upon graph neural networks (GNNs). The central scientific goal here is to learn features from corpora consisting of short texts for multiple document categorization and information extraction sub-tasks to improve the …
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Spatial-Temporal Data Modeling with Graph Neural Networks
… in a system. It aims to model the dynamic node-level inputs by assuming inter-dependency between connected nodes. A basic assumption behind spatial-temporal graph modeling is that a node's future information is conditioned on its historical information as well as its neighbors' historical …
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BEYOND LOCAL NEIGHBORHOODS: LEVERAGING INFORMATIVE NODES FOR IMPROVED GRAPH NEURAL NETWORKS PERFORMANCE
… as graphs, where entities are depicted as nodes and their relationships as edges. To analyze the properties of individual entities (node classification) or the community as a whole (graph classification), graph neural networks (GNNs) serve as a powerful tool. Most GNNs utilize a …
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Analyzing Networks with Hypergraphs: Detection, Classification, and Prediction
… performance in a variety of tasks, including node classification, link prediction, and influence modeling. However, these graph-based models struggle to capture high-order relations and interactions among entities effectively, leading them to underperform in many real-world scenarios. This …