Imbalanced node classification on graphs

Author: efir

August undefined, 2024

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. 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 …

A graph neural network-based node classification model on class ...

WitrynaDisease 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. small waxy nodule with pearly borders

GraphMixup: Improving Class-Imbalanced Node Classification by ...

Witryna21 cze 2024 · Recent years have witnessed great success in handling node classification tasks with Graph Neural Networks (GNNs). However, most 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. … Witryna4 sty 2024 · In some research hamilton2024inductive; zhou2024graph; tong2024directed, messages were passed along edges uniformly without accounting for priority of either graph structure or node attributes.Intuitively, each neighbor node’s impact was distinctive to the center node in the node classification task. Thus, attention-based … small waxed canvas messenger bag

Hyperbolic Geometric Graph Representation Learning for …

Class-Imbalanced Learning on Graphs: A Survey - ResearchGate

Witryna23 maj 2024 · This paper introduces a novel GNN-INCM model appropriate for node classification on class-imbalanced graph data. The proposed model optimizes two … Witryna14 kwi 2024 · Overall, we propose a multitask learning framework that predicts delivery time from two-view (classification and imbalanced regression). The main … small waxed paper bagsWitryna28 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 … hiking trails in mass

"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 … " - Imbalanced node classification on graphs

Imbalanced node classification on graphs

Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node …

WitrynaData-Level Methods Data Interpolation. GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction, in … 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. …

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Witrynatail classes. Currently, some works focus on imbalanced node classification on graphs. [23] over-samples the minority class by synthesizing more natural nodes as … 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 …

Witryna25 lis 2024 · The graph neural network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered. Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets … WitrynaExperiments on real-world imbalanced graph data demonstrate that BNE vastly outperforms the state-of-the-art methods for semi-supervised node classification on …

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 … 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 …

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 …

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 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 small way synonymWitryna21 paź 2024 · A new loss function FD-Loss is reconstructed based on the traditional algorithm-level approach to the imbalance problem, which can effectively solve the sample node imbalance problem and improve the classification accuracy by 4% compared to existing methods in the node classification task. Node classification … small waxing sticksWitrynaThe 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) … hiking trails in mcdowell county ncWitrynaAbstract Node classification for highly imbalanced graph data is challenging, with existing graph neural networks (GNNs) ... Highlights • A novel GNN-based … small ways synonymWitryna11 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 … small wax warmer for hair removalWitryna18 wrz 2024 · GraphMixup is presented, a novel mixup-based framework for improving class-imbalanced node classification on graphs that combines two context-based self-supervised techniques to capture both local and global information in the graph structure and a Reinforcement Mixup mechanism to adaptively determine how many samples … hiking trails in meigs county tnWitrynaMeanwhile, 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 … small wayfarer sunglasses