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 16 of 16 for “"Graph classification"”.
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A controlled sensing approach to graph classification
Graphs are used to model dependency structures, such as communication networks, social networks, and biological networks. Observing the graph in its entirety may be undesirable due to size of the graph or noise in observations, especially if only a function of the graph structure is of interest, …
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Attributed Graph Classification via Deep Graph Convolutional Neural Networks
From social networks to biological networks, graphs are a natural way to represent a diverse set of real-world data. This research presents attributed graph convolutional neural network with a pooling layer (AGCP for short), a novel end-to-end deep neural network model which captures the …
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Real-time analytics for complex structure data
… complex relationships are often represented as graphs to denote the content of the data entries and their structural relationships, where instances (nodes) are not only characterized by the content but are also subject to dependency relationships. Plus, real-time availability is one of …
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Scalable subgraph representation learning through simplification
Link prediction on graphs is a fundamental problem. Subgraph representation learning approaches (SGRLs), by transforming link prediction to graph classification on the subgraphs around the links, have achieved state-of-the-art performance in link prediction. However, SGRLs are computationally …
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Robust graph representation learning with structure-aware attention and self-supervised contrastive frameworks.
Graph-structured data is pervasive across domains such as social networks, biological systems, and information networks, yet effectively learning from such data remains a fundamental challenge in machine learning. My dissertation focuses on developing novel graph representation learning methods …
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Subgraph classification through neighborhood pooling
Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Graph neural networks (GNNs) are the de facto solution for node, link, and graph-level tasks but fail to perform well on subgraph …
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Deep Representation Learning on Labeled Graphs
We introduce recurrent collective classification (RCC), a variant of ICA analogous to recurrent neural network prediction. RCC accommodates any differentiable local classifier and relational feature functions. We provide gradient-based strategies for optimizing over model parameters to more …
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A Classification Approach for Automated Reasoning Systems--A Case Study in Graph Theory
<p>Reasoning systems which create classifications of structured objects face the problem of how object descriptions can be used to reflect their components as well as relations among these components. Current reasoning systems on graph theory do not adequately provide models to discover complex …
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Spatial-Temporal Data Modeling with Graph Neural Networks
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components 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 …
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Interpretable Network Representations
<p>Networks (or interchangeably graphs) have been ubiquitous across the globe and within science and engineering: social networks, collaboration networks, protein-protein interaction networks, infrastructure networks, among many others. Machine learning on graphs, especially network representation …
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BEYOND LOCAL NEIGHBORHOODS: LEVERAGING INFORMATIVE NODES FOR IMPROVED GRAPH NEURAL NETWORKS PERFORMANCE
… and scientific domains, can be represented 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 …
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Exploiting multimodality and structure in world representations
… Since the world abounds with structure, graph-based encodings are also likely to be incorporated in reasoning and decision-making modules. Furthermore, these relational representations are rather symbolic in nature---providing advantages over other formats, such as raw pixels---and can …
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On the Unique Tree Representation of Graphs
<p>This dissertation investigates classes of graphs which admit tree representations unique up to isomorphism. The definitions of these classes are based on local properties of P<sub>4</sub>'s, A template structure theorem is given which illustrates the nature of the local properties. The template …
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Deep Graph Representation Learning and its Application on Graph Clustering
Graphs like social networks, molecular graphs, and traffic networks are everywhere in the real world. Deep Graph Representation Learning (DGL) is essential for most graph applications, such as Graph Classification, Link Prediction, and Community Detection. DGL has made significant progress in …
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Domain-based Frameworks and Embeddings for Dynamics over Networks
… the network structure and depend on the geography as well. Traditional approaches either rely on models like Susceptible Infectious (SI) and Independent Cascade (IC) which are too restrictive because they focus only on single pathways or do not incorporate the model at all, resulting in …