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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Patterns amongst Competing Task Frequencies: Super-Linearities, and the Almond-DG Model
verfasst von : Danai Koutra, Vasileios Koutras, B. Aditya Prakash, Christos Faloutsos
Erschienen in: Advances in Knowledge Discovery and Data Mining
Verlag: Springer Berlin Heidelberg
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If Alice has double the friends of Bob, will she also have double the phone-calls (or wall-postings, or tweets)? Our first contribution is the discovery that the relative frequencies obey a power-law (sub-linear, or super-linear), for a wide variety of diverse settings: tasks in a phone-call network, like count of friends, count of phone-calls, total count of minutes; tasks in a twitter-like network, like count of tweets, count of followees etc. The second contribution is that we further provide a full, digitized 2-d distribution, which we call the
Almond-DG
model, thanks to the shape of its iso-surfaces. The
Almond-DG
model matches all our empirical observations: super-linear relationships among variables, and (provably) log-logistic marginals. We illustrate our observations on two large, real network datasets, spanning ~2.2
M
and ~3.1
M
individuals with 5 features each. We show how to use our observations to spot clusters and outliers, like, e.g., telemarketers in our phone-call network.