Discovering correlated spatio-temporal changes in evolving graphs
Knowledge and Information Systems
Characterizing sensor datasets with multi-granular spatio-temporal intervals
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Spatiotemporal neighborhood discovery for sensor data
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
Exploring multivariate spatio-temporal change in climate data using image analysis techniques
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Mining most frequently changing component in evolving graphs
World Wide Web
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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Graphs are adept at describing relational data, hence their popularity in fields including network management, webpage analysis and sociology. However, most of the current graph mining work regards graphs as static and unchanging, even though many graphs are dynamic. In this paper, we introduce a new pattern to discover from evolving graphs, namely regions of the graph that are evolving in a correlated manner. These regions of correlated spatio-temporal change group together graph changes that are topologically near (spatial) and evolve similarly (temporal) to each other. The regions can be used to summarise changes, particularly for graphs that have many simultaneous changes. We have developed an algorithm called cSTAG to summarise changes in dynamic graphs. This new algorithm discovers these regions of correlated change and identifies events that caused these changes. As a demonstration of the effectiveness of our algorithm, we applied cSTAG to summarise the changes to the Border Gateway Protocol connectivity graph during the 2005 Hurricane Katrina Disaster. cSTAG was able to identify the reported failures in Louisiana, as well as other simultaneous events.