Abstract
How do graphs look like? How do they evolve over time? How can we generate realistic-looking graphs? We review some static and temporal ’laws’, and we describe the “Kronecker” graph generator, which naturally matches all of the known properties of real graphs. Moreover, we present tools for discovering anomalies and patterns in two types of graphs, static and time-evolving. For the former, we present the ’CenterPiece’ subgraphs (CePS), which expects q query nodes (eg., suspicious people) and finds the node that is best connected to all q of them (eg., the master mind of a criminal group). We also show how to compute CenterPiece subgraphs efficiently. For the time evolving graphs, we present tensor-based methods, and apply them on real data, like the DBLP author-paper dataset, where they are able to find natural research communities, and track their evolution.
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© 2008 Springer-Verlag Berlin Heidelberg
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Faloutsos, C. (2008). Graph Mining: Laws, Generators and Tools. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_1
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DOI: https://doi.org/10.1007/978-3-540-68125-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
Online ISBN: 978-3-540-68125-0
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