Imbalanced node classification on graphs

Witryna26 cze 2024 · Learning unbiased node representations under class-imbalanced graph data is challenging due to interactions between adjacent nodes. Existing studies have in common that they compensate the minor class nodes ‘as a group’ according to their overall quantity (ignoring node connections in graph), which inevitably increase the … Witryna11 kwi 2024 · Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topological properties of the nodes (e.g., locations, roles) are unbalanced (topology-imbalance), other than the number of training labeled nodes …

Class-Imbalanced Learning on Graphs: A Survey - ResearchGate

Witryna18 wrz 2024 · Node classification is an important task in graph neural networks, but most existing studies assume that samples from different classes are balanced. … 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. … camper trailer with rooftop tent https://neo-performance-coaching.com

Hyperbolic Geometric Graph Representation Learning for …

Witryna8 mar 2024 · For example in imbalanced graph learning strategies, GraphSMOTE [10] addresses node imbalance by inserting new nodes of the minority classes into the … WitrynaData-Level Methods Data Interpolation. GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction, in … Witryna16 mar 2024 · 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 GNNs address the problem where node samples for different classes are balanced; while for many real-world scenarios, some classes … first texas homes farmers branch

GraphSMOTE: Imbalanced Node Classification on Graphs …

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Imbalanced node classification on graphs

Boosting-GNN: Boosting Algorithm for Graph Networks on …

Witryna1 gru 2024 · Graph Neural Networks (GNNs) have achieved unprecedented success in identifying categorical labels of graphs. However, most existing graph classification … Witryna24 maj 2024 · In recent decades, non-invasive neuroimaging techniques and graph theories have enabled a better understanding of the structural patterns of the human …

Imbalanced node classification on graphs

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WitrynaAbstract Node classification for highly imbalanced graph data is challenging, with existing graph neural networks (GNNs) ... Highlights • A novel GNN-based … WitrynaA novel hyperbolic geometric hierarchy-imbalance learning framework, named HyperIMBA, is proposed to alleviate the hierarchy-IMbalance issue caused by uneven hierarchy-levels and cross-hierarchy connectivity patterns of labeled nodes. Learning unbiased node representations for imbalanced samples in the graph has become a …

Witryna14 kwi 2024 · Classification of imbalanced big data has assembled an extensive consideration by many researchers during the last decade. Standard classification methods poorly diagnosis the minority class samples. Witryna9 kwi 2024 · In many real-world networks (e.g., social networks), nodes are associated with multiple labels and node classes are imbalanced, that is, some classes have significantly fewer samples than others.

WitrynaExisting methods are either tailored for non-graph structured data or designed specifically for imbalanced node classification while few focus on imbalanced graph classification. ... and Suhang Wang. 2024c. GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks. In WSDM. Google Scholar; … Witryna2 gru 2024 · In imbalanced node classification, the training process is dominated by majority nodes since they have a much larger population than minority nodes. ... Zhao, T., Zhang, X., Wang, S.: Tgraphsmote: imbalanced node classification on graphs with graph neural networks. In: Proceedings of the 14th International Conference on Web …

WitrynaExisting methods are either tailored for non-graph structured data or designed specifically for imbalanced node classification while few focus on imbalanced …

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 … first texas homes burleson txWitrynamainly 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 … first texas homes grayhawkWitrynaNode classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. … first texas homes houston txWitrynaExperiments on real-world imbalanced graph data demonstrate that BNE vastly outperforms the state-of-the-art methods for semi-supervised node classification on … first texas homes butler pantryWitryna3. A loss function for solving imbalanced graphs is introduced in the graph node classification task and achieves good results on several datasets. 2 Related Work … first texas homes farmers branch txWitryna21 cze 2024 · However, most existing GNNs are based on the assumption that node samples for different classes are balanced, while for many real-world graphs, there … first texas homes forney texasWitrynaMeanwhile, we propose intra-modality GCL by co-training non-pruned GNN and pruned GNN, to ensure node embeddings with similar attribute features stay closed. Last, we fine-tune the GNN encoder on downstream class-imbalanced node classification tasks. Extensive experiments demonstrate that our model significantly outperforms state-of … first texas homes hillcrest