Results 291 to 300 of about 229,000 (311)
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Learning Graph Matching with Graph Neural Networks
Graph matching aims at evaluating the dissimilarity of two graphs by defining a constrained correspondence between their nodes and edges. Error-tolerant graph matching, for instance, introduces the concept of a cost for penalizing structural differences in the matching.Kalvin Dobler, Kaspar Riesen
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Interpretable learning based Dynamic Graph Convolutional Networks for Alzheimer’s Disease analysis
Information Fusion, 2022Xiaofeng Zhu, Junbo Ma, Changan Yuan
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Graph representation learning in bioinformatics: trends, methods and applications
Briefings in Bioinformatics, 2022Hai-Cheng Yi +2 more
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Unsupervised Graph Embedding via Adaptive Graph Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Rui Zhang, Yunxing Zhang, Chengjun Lu
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Learnable graph convolutional network and feature fusion for multi-view learning
Information Fusion, 2023Zheyi Chen, Lele Fu, Jie Yao
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Graph Lifelong Learning: A Survey
IEEE Computational Intelligence Magazine, 2023Falih Gozi Febrinanto +2 more
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Multi-Modal Graph Learning for Disease Prediction
IEEE Transactions on Medical Imaging, 2022Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu
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Deep graph learning for semi-supervised classification
Pattern Recognition, 2021Guangfeng Lin +2 more
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Graph Learning for Multiview Clustering
IEEE Transactions on Cybernetics, 2018Kun Zhan, Changqing Zhang
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