Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces

Graph models are relevant in many fields, such as distributed computing, intelligent tutoring systems or social network analysis. In many cases, such models need to take changes in the graph structure into account, i.e. a varying number of nodes or edges... (read more)

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Methods used in the Paper

Gaussian Process
Non-Parametric Classification