Results 11 to 20 of about 263,942 (258)
Mixup for Node and Graph Classification [PDF]
Proceedings of the Web Conference 2021, 2021 Mixup is an advanced data augmentation method for training neural network based image classifiers, which interpolates both features and labels of a pair of images to produce synthetic samples. However, devising the Mixup methods for graph learning is challenging due to the irregularity and connectivity of graph data. In this paper, we propose the Mixup
Yiwei Wang 0001 +4 more
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Node classification in uncertain graphs [PDF]
Proceedings of the 26th International Conference on Scientific and Statistical Database Management, 2014 In many real applications that use and analyze networked data, the links in the network graph may be erroneous, or derived from probabilistic techniques. In such cases, the node classification problem can be challenging, since the unreliability of the links may affect the final results of the classification process.
Dallachiesa, Michele +2 more
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Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities
Mathematics, 2022 Nowadays, graph representation learning methods, in particular graph neural network methods, have attracted great attention and performed well in many downstream tasks. However, most graph neural network methods have a single perspective since they start
Xing Li +3 more
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Network Representation Learning With Community Awareness and Its Applications in Brain Networks
Frontiers in Physiology, 2022 Previously network representation learning methods mainly focus on exploring the microscopic structure, i.e., the pairwise relationship or similarity between nodes.
Min Shi, Bo Qu, Xiang Li, Cong Li
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Uncertainty Propagation in Node Classification
2022 IEEE International Conference on Data Mining (ICDM), 2022 Quantifying predictive uncertainty of neural networks has recently attracted increasing attention. In this work, we focus on measuring uncertainty of graph neural networks (GNNs) for the task of node classification. Most existing GNNs model message passing among nodes. The messages are often deterministic.
Xu, Zhao +3 more
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Unsupervised Graph Representation Learning With Variable Heat Kernel
IEEE Access, 2020 Graph representation learning aims to learn a low-dimension latent representation of nodes, and the learned representation is used for downstream graph analysis tasks. However, most of the existing graph embedding models focus on how to aggregate all the
Yongjun Jing +4 more
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Bayesian Node Classification for Noisy Graphs [PDF]
2021 IEEE Statistical Signal Processing Workshop (SSP), 2021 Graph neural networks (GNN) have been recognized as powerful tools for learning representations in graph structured data. The key idea is to propagate and aggregate information along edges of the given graph. However, little work has been done to analyze the effect of noise on their performance.
Hakim Hafidi +3 more
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Virtual Node Tuning for Few-shot Node Classification
CoRR, 2023 Accepted to KDD ...
Zhen Tan 0001 +3 more
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One Node at a Time: Node-Level Network Classification
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021 8 pages, 5 ...
Saray Shai, Isaac Jacobs, Peter J. Mucha
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Applied Sciences, 2023 In recent years, graph neural networks (GNNs) have achieved great success in handling node classification tasks. However, as data explosively grows in various industries, the problem of class imbalance becomes increasingly severe.
Liying Zhang, Haihang Sun
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