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Structure Prediction in Temporal Networks using Frequent Subgraphs

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2 Author(s)
Mayank Lahiri ; Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607. Email: ; Tanya Y. Berger-Wolf

There are several types of processes which can be modeled explicitly by recording the interactions between a set of actors over time. In such applications, a common objective is, given a series of observations, to predict exactly when certain interactions will occur in the future. We propose a representation for this type of temporal data and a generic, streaming, adaptive algorithm to predict the pattern of interactions at any arbitrary point in the future. We test our algorithm on predicting patterns in e-mail logs, correlations between stock closing prices, and social grouping in herds of Plains zebras. Our algorithm averages over 85% accuracy in predicting a set of interactions at any unseen timestep. To the best of our knowledge, this is the first algorithm that predicts interactions at the finest possible time grain

Published in:

Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on

Date of Conference:

March 1 2007-April 5 2007