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Graph Learning: A Survey [PDF]
IEEE Transactions on Artificial Intelligence, 2021 Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information ...
Feng Xia, Shuo Yu, Abdul Aziz
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2020
We introduce an overview of methods for learning in structured domains covering foundational works developed within the last twenty years to deal with a whole range of complex data representations, including hierarchical structures, graphs and networks, and giving special attention to recent deep learning models for graphs.
Bacciu D., Micheli A.
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We introduce an overview of methods for learning in structured domains covering foundational works developed within the last twenty years to deal with a whole range of complex data representations, including hierarchical structures, graphs and networks, and giving special attention to recent deep learning models for graphs.
Bacciu D., Micheli A.
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2009
Motivated by a problem of targeted advertising in social networks, we introduce and study a new model of online learning on labeled graphs where the graph is initially unknown, and the algorithm is free to choose the next vertex to predict. After observing that natural nonadaptive exploration/prediction strategies (like depth-first with majority vote ...
Cesa Bianchi N +2 more
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Motivated by a problem of targeted advertising in social networks, we introduce and study a new model of online learning on labeled graphs where the graph is initially unknown, and the algorithm is free to choose the next vertex to predict. After observing that natural nonadaptive exploration/prediction strategies (like depth-first with majority vote ...
Cesa Bianchi N +2 more
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2007 IEEE 11th International Conference on Computer Vision, 2007
As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs.
Tibério S. Caetano +3 more
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As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs.
Tibério S. Caetano +3 more
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Neural Networks, 2020
Transform learning is a new representation learning framework where we learn an operator/transform that analyses the data to generate the coefficient/representation. We propose a variant of it called the graph transform learning; in this we explicitly account for the correlation in the dataset in terms of graph Laplacian.
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Transform learning is a new representation learning framework where we learn an operator/transform that analyses the data to generate the coefficient/representation. We propose a variant of it called the graph transform learning; in this we explicitly account for the correlation in the dataset in terms of graph Laplacian.
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Optimization Letters, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Huilan Chang +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Huilan Chang +2 more
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AGCL: Adaptive Graph Contrastive Learning for graph representation learning
Neurocomputing, 2023Jiajun Yu, Adele Lu Jia
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MGLNN: Semi-supervised learning via Multiple Graph Cooperative Learning Neural Networks
Neural Networks, 2022Bo Jiang, Beibei Wang
exaly