Complex Networks: Classification and Dynamics

Nishant Malik

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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