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The paper presents a framework for semi-supervised nonlinear embedding methods useful for exploratory analysis and visualization of spatio-temporal network data. The method provides a functional embedding based on a neural network optimizing the graph-based cost function. It exploits an online stochastic gradient descent which, avoiding the costly matrix computations and the out-of-sample problem, makes it naturally applicable for large-scale dynamic spatiotemporal problems. The semi-superviseddoi:10.1145/1653771.1653822 dblp:conf/gis/Pozdnoukhov09 fatcat:uwv4e4ni3vd65gtnhvdnfbxt3e