Results 21 to 30 of about 263,942 (258)
Joint Use of Node Attributes and Proximity for Node Classification
, 2022 Peer ...
Arpit Merchant, Michael Mathioudakis
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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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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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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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Particle Propagation Model for Dynamic Node Classification
IEEE Access, 2020 With the popularity of online social networks, researches on dynamic node classification have received further attention. Dynamic node classification also helps the rapid popularization of online social networks.
Wenzheng Li +4 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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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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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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A Classification of Nodes for Structural Controllability
IEEE Transactions on Automatic Control, 2019 In this paper, we consider (large and complex) interconnected networks. We assume that each state/node, not belonging to a set of forbidden nodes of the network, can be selected to act as a steering node, meaning that such a node then is influenced by its own individual control.
Christian Commault, Jacob van der Woude
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SNOC: Streaming Network Node Classification [PDF]
2014 IEEE International Conference on Data Mining, 2014 Many real-world networks are featured with dynamic changes, such as new nodes and edges, and modification of the node content. Because changes are continuously introduced to the network in a streaming fashion, we refer to such dynamic networks as streaming networks.
Ting Guo 0005 +3 more
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