Mathematics, Dartmouth College.
Many complex systems in nature and society are studied using networks. A
classical problem in networks is their classification. Network theory
provides many analytical tools for this task. However, application of these
tools to real-world data is challenging. We present a new, hybrid approach
to network classification, combining a manual selection of features of
potential interest with automated classification methods. We demonstrate
the broad applicability of this approach by classifying days of the week
from call detail records and diagnosing types of cancer tumors based on
their transcription factor-gene regulatory networks.
Additionally, we explore the role of transitivity reinforcement in the co-evolving voter model. We will present a semi-analytical framework of approximate master equations to predict the dynamical behaviors of the model for a variety of parameter settings.
* Joint work with Ian Barnett, J.P. Onnela, Peter Mucha, Feng Shi and H-W. Lee.
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