Witryna18 wrz 2024 · In recent years, the node classification task in graph neural networks (GNNs) has developed rapidly, driving the development of research in various fields. … Witryna17 mar 2024 · Graphs are becoming ubiquitous across a large spectrum of real-world applications in the forms of social networks, citation networks, telecommunication networks, biological networks, etc. [].For a considerable number of real-world graph node classification tasks, the training data follows a long-tail distribution, and the …
A graph neural network-based node classification model on class ...
Witryna28 paź 2024 · The GAT algorithm supports representation learning and node classification for homogeneous graphs. There are versions of the graph attention layer that support both sparse and dense adjacency matrices. Graph Convolutional Network (GCN) [6] The GCN algorithm supports representation learning and node … Witryna11 kwi 2024 · However, recent studies have shown that GNNs tend to give an unsatisfying performance on minority nodes (nodes of minority classes) when trained on imbalanced graph datasets [3].This limitation may severely hinder their capability in some classification tasks, since node classes are often severely imbalanced in … gypsum by mccarthy syracuse ny
Class-Imbalanced Learning on Graphs: A Survey - Semantic Scholar
Witryna15 mar 2024 · Abstract. Node classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. However, existing ... WitrynaNode classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. … WitrynaAbstract Node classification for highly imbalanced graph data is challenging, with existing graph neural networks (GNNs) ... Highlights • A novel GNN-based … br4c5