Ground Metric Learning on Graphs
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Mathematical Imaging and Vision"
DOI: 10.1007/s10851-020-00996-z
Abstract: Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between observations. Their relevance for real-life applications strongly hinges on whether that ground metric parameter is suitably chosen. The challenge… read more here.
Keywords: learning graphs; metric learning; ground; problem ... See more keywords
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs
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DOI: 10.1016/j.neucom.2021.08.028
Abstract: The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification,… read more here.
Keywords: representation; learning graphs; graphs using; representation learning ... See more keywords
Deep Learning on Graphs: A Survey
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DOI: 10.1109/tkde.2020.2981333
Abstract: Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the… read more here.
Keywords: graphs survey; learning graphs; learning; deep learning ... See more keywords
Fair Contrastive Learning on Graphs
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2022.3174953
Abstract: Node representation learning plays a critical role in learning over graphs. Specifically, the success of contrastive learning methods in unsupervised node representation learning has been demonstrated for various tasks, which has led to increase in… read more here.
Keywords: learning graphs; fairness; fair contrastive; contrastive learning ... See more keywords
Adaptive Diffusions for Scalable Learning Over Graphs
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DOI: 10.1109/tsp.2018.2889984
Abstract: Diffusion-based classifiers such as those relying on the Personalized PageRank and the heat kernel enjoy remarkable classification accuracy at modest computational requirements. Their performance however is affected by the extent to which the chosen diffusion… read more here.
Keywords: learning graphs; scalable learning; adaptive diffusions; diffusion ... See more keywords