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A channel water temperature prediction method based on transfer learning and spatial-temporal graph neural networks. [PDF]

Sci Rep
Lu H, Tian Y, Weng P, Qiao Y.
europepmc   +1 more source

Heterogeneous graph neural networks reveal molecular mechanisms of folate deficiency in placental insufficiency through multiomics integration. [PDF]

Sci Rep
Xie X, Li Z, Xiao Q, Xiong H, Yuan M.
europepmc   +1 more source

Integrative Spatial Modelling of Cellular Plasticity using Graph Neural Networks and Geostatistics

Withnell E, Celik C, Secrier M.
europepmc   +1 more source

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Graph Mining with Graph Neural Networks

Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021
Graphs are ubiquitous data structures in various fields, such as social media, transportation, linguistics and chemistry. To solve downstream graph-related tasks, it is of great significance to learn effective representations for graphs. My research strives to help meet this demand; due to the huge success of deep learning methods, especially graph ...
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Graph Neural Networks in Cheminformatics

2021
Graph neural networks represent nowadays the most effective machine learning technology in the biochemistry domain. Learning on the huge amount of chemical data can take an important part in finding new molecules or new drugs, which is a crucial research work in cheminformatics.
H. N. Tran Tran, J. Joshua Thomas, Nurul Hashimah Ahamed Hassain Malim, Abdalla M. Ali, Bach Huynh Son   +4 more
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Neural Networks Are Graphs! Graph Neural Networks for Equivariant Processing of Neural Networks

2023
Neural networks that can process the parameters of other neural networks find applications in diverse domains, including processing implicit neural representations, domain adaptation of pretrained networks, generating neural network weights, and predicting generalization errors.
Zhang, D.W., Kofinas, M., Zhang, Y., Chen, Y., Burghouts, G.J., Snoek, C.G.M.   +5 more
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Graph Ensemble Neural Network

Information Fusion, 2023
Rui Duan 0003, Chungang Yan, Junli Wang 0001, Changjun Jiang 0002   +3 more
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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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