Recovery of missing information in graph sequences by means of reference pattern matching and decision tree learning

  • Authors:
  • Horst Bunke;Peter Dickinson;Christophe Irniger;Miro Kraetzl

  • Affiliations:
  • Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland;Intelligence, Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh SA 5111, Australia;Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland;Intelligence, Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh SA 5111, Australia

  • Venue:
  • Pattern Recognition
  • Year:
  • 2006

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Abstract

Algorithms for the analysis of graph sequences are proposed in this paper. In particular, we study the problem of recovering missing information and predicting the occurrence of nodes and edges in time series of graphs. Two different recovery schemes are developed. The first scheme uses reference patterns that are extracted from a training set of graph sequences, while the second method is based on decision tree induction. Our work is motivated by applications in computer network analysis. However, the proposed recovery and prediction schemes are generic and can be applied in other domains as well.