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CIDM
2007
IEEE

Structure Prediction in Temporal Networks using Frequent Subgraphs

13 years 10 months ago
Structure Prediction in Temporal Networks using Frequent Subgraphs
— 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.
Mayank Lahiri, Tanya Y. Berger-Wolf
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where CIDM
Authors Mayank Lahiri, Tanya Y. Berger-Wolf
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