WebJan 1, 2024 · @article{Tygesen2024UnboxingTG, title={Unboxing the graph: Towards interpretable graph neural networks for transport prediction through neural relational inference}, author={Mathias Niemann Tygesen and Francisco Camara Pereira and Filipe Rodrigues}, journal={Transportation Research Part C: Emerging Technologies}, … WebHowever, many implicit assumptions of GCNs do not apply to road networks. We introduce the Relational Fusion Network (RFN), a novel type of GCN designed specifically for road …
Multirelational Tensor Graph Attention Networks for Knowledge Fusion …
WebNov 11, 2024 · The graph-convolution block was used to extract the local spatial features of the road network, the fusion block was used to fuse global features and different local ... Jensen, C.S.; Nielsen, T.D. Relational Fusion Networks: Graph Convolutional Networks for Road Networks. IEEE Trans. Intell. Transp. Syst. 2024. [Google Scholar ... WebRelational Fusion Networks. This library contains a reference implementation of the Relational Fusion Network (RFN). The RFN first appeared in a paper presented at ACM SIGSPATIAL 2024 [1] which is available through the ACM Digital Library.An extended version of this paper has since appeared in IEEE Transactions on Intelligent Transportation … find max value in an array
[2304.06336] Attributed Multi-order Graph Convolutional Network …
WebAug 30, 2024 · Relational Fusion Network (RFN) aim to address the shortcomings of. state-of-the-art GCNs in the context of machine learning on road. networks. The basic premise … WebAug 30, 2024 · We introduce the notion of Relational Fusion Network (RFN), a novel type of GCN designed specifically for machine learning on road networks. In particular, we propose methods that outperform state-of-the-art GCNs on both a road segment regression task and a road segment classification task by 32-40% and 21-24%, respectively. WebHowever, many implicit assumptions of GCNs do not apply to road networks. We introduce the Relational Fusion Network (RFN), a novel type of GCN designed specifically for road networks. In particular, we propose methods that outperform state-of-the-art GCNs by 21%-40% on two machine learning tasks in road networks. find max value in 2d array c++