scholar.google.com › citations
Dec 21, 2023 · The Feature Selection Graph Neural Network (FSGNN), a straightforward and shallow model, is framed by combining these approaches. Nine standard ...
Apr 26, 2024 · Abstract—Graph Neural Networks (GNNs) is one of the most essential tools for learning from graph-structured data.
Jan 25, 2023 · Graphs help to define the relationships between entities in the data. These relationships, represented by edges, often provide additional ...
Feature Selection: Key to Enhance Node Classification with Graph Neural Networks ... Graphs help to define the relationships between entities in the data. These ...
Missing: Optimized Categorization.
Feature selection in graph neural networks ... On any given graph-structured data, a set of features can be generated for the nodes (e.g. using Eqs. (1) & (2)).
Missing: Categorization. | Show results with:Categorization.
In this paper, we present an investigation of active learning with GNNs for node classification tasks. Specifically, we propose a new method, which uses node ...
We propose an algorithm to rank the extracted features in the sense that when using them for the same classification problem, the accuracy goes down gradually ...
Missing: Categorization. | Show results with:Categorization.
In this work, we show that selective aggregation of node features from various hops leads to better performance than default aggregation on the node ...
Jun 15, 2023 · In this paper, we propose the graph neural network model ENode-GAT for improving the accuracy of small sample node classification using the ...
People also ask
Can neural networks be used for feature selection?
What is node classification using GNNs?
What is feature selection in convolutional neural networks?
Why graph neural networks for recommender systems?
In this paper, we propose a graph neural network-based feature selection method – GRAph. Convolutional nEtwork feature Selector (GRACES) – to extract features ...
Missing: Categorization. | Show results with:Categorization.