Journal article

Discovering correlated spatio-temporal changes in evolving graphs

Jeffrey Chan, James Bailey, Christopher Leckie

KNOWLEDGE AND INFORMATION SYSTEMS | SPRINGER LONDON LTD | Published : 2008

Abstract

Graphs provide powerful abstractions of relational data, and are widely used in fields such as network management, web page analysis and sociology. While many graph representations of data describe dynamic and time evolving relationships, most graph mining work treats graphs as static entities. Our focus in this paper is to discover regions of a graph that are evolving in a similar manner. To discover regions of correlated spatio-temporal change in graphs, we propose an algorithm called cSTAG. Whereas most clustering techniques are designed to find clusters that optimise a single distance measure, cSTAG addresses the problem of finding clusters that optimise both temporal and spatial distanc..

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