Recovery of missing information in graph sequences

Authors:
Horst Bunke;Peter Dickinson;Miro Kraetzl
Affiliations:
Institut für Informatik und angewandte Mathematik, Universität Bern, Bern, Switzerland;Intelligence, Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh, SA, Australia;Intelligence, Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh, SA, Australia
Venue:
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Year:
2005

Citing 8
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Predictive data mining: a practical guide

Predictive data mining: a practical guide
Introduction to the Special Section on Graph Algorithms in Computer Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
News Sensitive Stock Trend Prediction

PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications

MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Optimizing Similarity Search for Arbitrary Length Time Series Queries

IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition

GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
On graphs with unique node labels

GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Novel 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. 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.