Improving the Performance of Graph Filters and Learnable Graph Filters in Graph Neural Networks, M.Sc. Thesis Sharif University of Technology ; Babaiezadeh, Masoud (Supervisor)
Abstract
Graph signals are sets of values residing on sets of nodes that are connected via edges. Graph Neural Networks (GNNs) are a type of machine learning model for working with graph-structured data, such as graph signals. GNNs have applications in graph classification, node classification, and link prediction. They can be thought of as learnable filters. In this thesis, our focus is on graph filters and enhancing the performance of GNNs. In the first part, we aim to reduce computational costs in graph signal processing, particularly in graph filters. We explore methods to transform signals to the frequency domain with lower computational cost. In the latter part, we examine regulations in...
Cataloging briefImproving the Performance of Graph Filters and Learnable Graph Filters in Graph Neural Networks, M.Sc. Thesis Sharif University of Technology ; Babaiezadeh, Masoud (Supervisor)
Abstract
Graph signals are sets of values residing on sets of nodes that are connected via edges. Graph Neural Networks (GNNs) are a type of machine learning model for working with graph-structured data, such as graph signals. GNNs have applications in graph classification, node classification, and link prediction. They can be thought of as learnable filters. In this thesis, our focus is on graph filters and enhancing the performance of GNNs. In the first part, we aim to reduce computational costs in graph signal processing, particularly in graph filters. We explore methods to transform signals to the frequency domain with lower computational cost. In the latter part, we examine regulations in...
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