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2012 16th Panhellenic Conference on Informatics (2012)
Piraeus, Greece Greece
Oct. 5, 2012 to Oct. 7, 2012
ISBN: 978-1-4673-2720-6
pp: 144-149
How can we spot anomalies in large, time-evolving graphs? When we have multi-aspect data, e.g. who published which paper on which conference and on what year, how can we combine this information, in order to obtain good summaries thereof and unravel hidden anomalies and patterns? Such multi-aspect data, including time-evolving graphs, can be successfully modelled using Tensors. In this paper, we show that when we have multiple dimensions in the dataset, then tensor analysis is a powerful and promising tool. Our method TENSORSPLAT, at the heart of which lies the "PARAFAC" decomposition method, can give good insights about the large networks that are of interest nowadays, and contributes to spotting micro-clusters, changes and, in general, anomalies. We report extensive experiments on a variety of datasets (co-authorship network, time-evolving DBLP network, computer network and Facebook wall posts) and show how tensors can be proved useful in detecting "strange" behaviors.
Tensile stress, Matrix decomposition, Vectors, Data mining, Facebook, Switches

C. Faloutsos, D. Koutra and E. E. Papalexakis, "TensorSplat: Spotting Latent Anomalies in Time," 2012 16th Panhellenic Conference on Informatics(PCI), Piraeus, Greece Greece, 2012, pp. 144-149.
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