Results 11 to 20 of about 2,499,483 (247)
The sophisticated architecture of the rat atrioventricular node [PDF]
, 2005 In the mammalian heart, the action potential spontaneously generated in the sinoatrial (SA) node propagates through the atria to reach the atrioventricular (AV) node.
Yoo, Shin
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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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GATSMOTE: Improving Imbalanced Node Classification on Graphs via Attention and Homophily
Mathematics, 2022 In recent decades, non-invasive neuroimaging techniques and graph theories have enabled a better understanding of the structural patterns of the human brain at a macroscopic level.
Yongxu Liu, Zhi Zhang, Yan Liu, Yao Zhu
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Robust cross-network node classification via constrained graph mutual information
, 2022 The recent methods for cross-network node classification mainly exploit graph neural networks (GNNs) as feature extractor to learn expressive graph representations across the source and target graphs.
Yang, Shuiqiao +6 more
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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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Imbalanced Node Classification Beyond Homophilic Assumption
, 2023 Imbalanced node classification widely exists in real-world networks where graph neural networks (GNNs) are usually highly inclined to majority classes and suffer from severe performance degradation on classifying minority class nodes.
Wang, G +5 more
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One Node at a Time: Node-Level Network Classification [PDF]
, 2022 Network classification aims to group networks (or graphs) into distinct categories based on their structure. We study the connection between classification of a network and of its constituent nodes, and whether nodes from networks in different groups are
Mucha, Peter J. +2 more
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CE-Net: A Coordinate Embedding Network for Mismatching Removal
IEEE Access, 2021 Mismatching removal is at the core yet still a challenging problem in the photogrammetry and computer vision field. In this paper, we propose a coordinate embedding network (named CE-Net).
Shiyu Chen +5 more
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Joint Use of Node Attributes and Proximity for Node Classification [PDF]
, 2022 Node classification aims to infer unknown node labels from known labels and other node attributes. Standard approaches for this task assume homophily, whereby a node’s label is predicted from the labels of other nodes nearby in the network.
Merchant, Arpit +3 more
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Semi-AttentionAE: An Integrated Model for Graph Representation Learning
IEEE Access, 2021 Graph embedding learns low-dimensional vector representations which capture and preserve information in original graphs. Common shallow neural networks and deep autoencoder only use adjacency matrix as input, and usually ignore node attributes and ...
Lining Yuan +3 more
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