A Query Based Approach for Mining Evolving Graphs

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Abstract:

An evolving graph is a graph that can change over time. Such graphs can be applied in modelling a wide range of real-world phenomena, like computer networks, social networks and protein interaction networks. This paper addresses the novel problem of querying evolving graphs using spatio-temporal patterns. In particular, we focus on answering selection queries, which can discover evolving subgraphs that satisfy both a temporal and a spatial predicate. We investigate the efficient implementation of such queries and experimentally evaluate our techniques using real-world evolving graph datasets, Internet connectivity logs and the Enron email corpus. We show that is possible to use queries to discover meaningful events hidden in this data and demonstrate that our implementation is scalable for very large evolving graphs.

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