Abstract
This paper is concerned with time series of graphs and proposes a novel scheme that is able to predict the presence or absence of nodes in a graph. The proposed scheme is based on decision trees that are induced from a training set of sample graphs. The work is motivated by applications in computer network monitoring. However, the proposed prediction method is generic and can be used in other applications as well. Experimental results with graphs derived from real computer networks indicate that a correct prediction rate of up to 97% can be achieved.
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Bunke, H., Dickinson, P., Irniger, C., Kraetzl, M. (2005). Analysis of Time Series of Graphs: Prediction of Node Presence by Means of Decision Tree Learning. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_36
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DOI: https://doi.org/10.1007/11510888_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
Online ISBN: 978-3-540-31891-0
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