Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces

@article{Paaen2017TimeSP,
  title={Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces},
  author={Benjamin Paa{\ss}en and Christina G{\"o}pfert and Barbara Hammer},
  journal={Neural Processing Letters},
  year={2017},
  volume={48},
  pages={669-689}
}
Graphs are a flexible and general formalism providing rich models in various important domains, 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, that is, changes in the number of nodes or in the graph connectivity. Predicting such changes within graphs can be expected to yield important insight with respect to the underlying dynamics, e.g. with respect to user behaviour… CONTINUE READING
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