Results 21 to 30 of about 748,129 (177)
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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DAG: Dual Attention Graph Representation Learning for Node Classification
Mathematics, 2023 Transformer-based graph neural networks have accomplished notable achievements by utilizing the self-attention mechanism for message passing in various domains. However, traditional methods overlook the diverse significance of intra-node representations,
Siyi Lin, Jie Hong, Bo Lang, Lin Huang
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Multi-engine packet classification hardware accelerator [PDF]
, 2009 As line rates increase, the task of designing high performance architectures with reduced power consumption for the processing of router traffic remains important. In this paper, we present a multi-engine packet classification hardware accelerator, which
Kennedy, Alan +3 more
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Joint Use of Node Attributes and Proximity for Node Classification
, 2022 Peer ...
Arpit Merchant, Michael Mathioudakis
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A-B Nodes Classification for Power Estimation [PDF]
2006 International Conference on Field Programmable Logic and Applications, 2006 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work
Elias Todorovich, Eduardo I. Boemo
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Deep Graph-Convolutional Generative Adversarial Network for Semi-Supervised Learning on Graphs
Remote Sensing, 2023 Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on the attribute information and the structure information of graph data.
Nan Jia +3 more
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Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks [PDF]
, 2018 In this letter, we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures.
Chen, Zhaoxi +3 more
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Exploring Edge Disentanglement for Node Classification
Proceedings of the ACM Web Conference 2022, 2022 Accepted to The Web Conference (WWW ...
Tianxiang Zhao 0001 +2 more
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CLNode: Curriculum Learning for Node Classification
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023 Node classification is a fundamental graph-based task that aims to predict the classes of unlabeled nodes, for which Graph Neural Networks (GNNs) are the state-of-the-art methods. Current GNNs assume that nodes in the training set contribute equally during training.
Xiaowen Wei +5 more
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Structural Hierarchy-Enhanced Network Representation Learning
Applied Sciences, 2020 Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP).
Cheng-Te Li, Hong-Yu Lin
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