defense: Learning Hierarchical Workflows Using Community Detection, 4/18

MS Thesis Defense

Learning Hierarchical Workflows Using Community Detection

Akshay Peshave

1:00pm Friday, 18 April 2014, ITE 325b

Workflows identified from user event logs and click-stream data are useful as knowledge bases for behavioral analysis and recommendation systems. In this study we identify abstractions or summaries of event logs modeled as user activity flow networks. The abstractions are identified based on structural properties as well as user activity flow dynamics over the network using community detection methods. We apply a fast modularity optimization and multi-level resolution approach to detect hierarchical community structure in user activity flow networks. The detected communities are compared to those detected by the information-theoretic map equation minimization approach to weigh pros and cons of the fast modularity optimization approach in the workflows context. We further attempt to identify the most probable sources and sinks of user activity in individual communities and trim the network accordingly to reduce entropy of the workflow abstractions.

Committee: Drs. Tim Oates (chair), Matt Schmill and Tim Finin

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