Imbalanced node classification on graphs
WitrynaA curated list of papers and code related to class-imbalanced learning on graphs (CILG). - CILG-Papers/README.md at main · yihongma/CILG-Papers WitrynaData-Level Methods Data Interpolation. GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction, in ECML/PKDD 2024.. GATSMOTE: Improving Imbalanced Node Classification on Graphs via Attention and Homophily, in Mathematics 2024.. Graph Neural Network …
Imbalanced node classification on graphs
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WitrynaNode classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. … 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 …
WitrynaTo overcome the above problem, in this paper, a new graph neural network model adapted to node classification on imbalanced graph datasets is proposed, i.e., the dual cost-sensitive graph convolutional network (DCSGCN). To the best of our knowledge, our study is among the first to be devoted to an imbalanced graph node … Witrynatail classes. Currently, some works focus on imbalanced node classification on graphs. [23] over-samples the minority class by synthesizing more natural nodes as …
WitrynaNode classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. … Witryna25 maj 2024 · nodes with a highly similar feature space and label space. • We conduct extensive experiments involving an imbalanced node classification task. Experimental results demonstrate that our proposed framework can achieve state-of-the-art performance on imbalanced node classification. 2. Related Work and Methods 2.1. …
WitrynaThe imbalanced data classification problem has aroused lots of concerns from both academia and industrial since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) …
Witrynamainly focus on the setting that node classes are balanced. In many real-world applications, node classes could be imbal-anced in graphs, i.e., some classes … eagle cliff apartments carrollton gaWitryna11 kwi 2024 · However, recent studies have shown that GNNs tend to give an unsatisfying performance on minority nodes (nodes of minority classes) when … eagle cliff elementary billings mtWitryna28 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 … csi coop randallstown villaWitrynaData-Level Methods Data Interpolation. GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction, in … csi cool changeWitryna15 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 ... csicouncilWitrynamainly focus on the setting that node classes are balanced. In many real-world applications, node classes could be imbal-anced in graphs, i.e., some classes have signicantly fewer samples for training than other classes. For example, for fake account detec-tion [25, 42], the majority of users in a social network platform are eagle cliff hike mohonkWitrynaDisease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients' features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. eagle cliff golf course