Imbalanced node classification on graphs
WitrynaNode classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. … 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.
Imbalanced node classification on graphs
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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 … Witryna21 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 …
Witrynatail classes. Currently, some works focus on imbalanced node classification on graphs. [23] over-samples the minority class by synthesizing more natural nodes as … WitrynaAbstract Node classification for highly imbalanced graph data is challenging, with existing graph neural networks (GNNs) ... Highlights • A novel GNN-based …
Witryna11 kwi 2024 · However, recent studies have shown that GNNs tend to give an unsatisfying performance on minority nodes (nodes of minority classes) when … 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.
Witryna23 maj 2024 · Node classification for highly imbalanced graph data is challenging, with existing graph neural networks (GNNs) typically utilizing a balanced class distribution …
Witryna9 kwi 2024 · A comprehensive understanding of the current state-of-the-art in CILG is offered and the first taxonomy of existing work and its connection to existing … green cheap purses on loanWitryna11 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 ... flow light grey sk713rWitryna14 kwi 2024 · Overall, we propose a multitask learning framework that predicts delivery time from two-view (classification and imbalanced regression). The main … flowlight fsWitrynaMeanwhile, 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 … green check box transparentWitrynaExisting 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; … green check and red crossWitryna18 wrz 2024 · Node classification is an important task in graph neural networks, but most existing studies assume that samples from different classes are balanced. … flowlightsWitryna26 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 … green check box image