We propose a novel deep learning framework, STGCN, to tackle time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem ...

Spectral graph theory examines the structural and dynamical properties of graphs by analysing the spectra of associated matrices such as the adjacency matrix, Laplacian, normalised Laplacian and ...

Table 1: Performance comparison of different approaches on the dataset PeMSD7. Fig. 3: Speed prediction in the morning peak and evening rush hours of the dataset ...

Abstract: This paper concerns the study of observability in consensus networks modeled with strongly regular graphs or distance regular graphs. We first give a Kalman-like simple algebraic criterion ...

According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...

Abstract: In human pose estimation methods based on graph convolutional architectures, the human skeleton is usually modeled as an undirected graph whose nodes are body joints and edges are ...

Urban transportation destination prediction is a crucial issue in the area of intelligent transportation, such as urban traffic planning and traffic congestion control. The spatial structure of the ...