WebSpecifically, we use two widely used and open-source GNN algorithms, namely Temporal Graph Convolutional Network (TGCN) and Diffusion Convolutional Recurrent Neural … WebApr 13, 2024 · The short-term bus passenger flow prediction of each bus line in a transit network is the basis of real-time cross-line bus dispatching, which ensures the efficient utilization of bus vehicle resources. As bus passengers transfer between different lines, …

An Invertible Graph Diffusion Neural Network for Source Localization

WebMay 21, 2024 · The success of graph neural networks (GNNs) largely relies on the process of aggregating information from neighbors defined by the input graph structures. Notably, message passing based GNNs, e.g., graph convolutional networks, leverage the immediate neighbors of each node during the aggregation process, and recently, graph diffusion … http://proceedings.mlr.press/v139/chamberlain21a/chamberlain21a.pdf potchari

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WebApr 13, 2024 · The short-term bus passenger flow prediction of each bus line in a transit network is the basis of real-time cross-line bus dispatching, which ensures the efficient utilization of bus vehicle resources. As bus passengers transfer between different lines, to increase the accuracy of prediction, we integrate graph features into the recurrent … WebSep 19, 2024 · Graph Neural Diffusion. Graph Neural Networks (GNNs) learn by performing some form of message passing on the graph, whereby features are passed from node to node across the edges. Such a mechanism is related to diffusion processes on graphs that can be expressed in the form of a partial differential equation (PDE) called … WebHighlight: We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE. 2. Directional Graph Networks. potchara u-thong