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Network Analysis: Overview and Applications To Population Science

a Workshop

Ceren Budak (School of Information)
Daniel Romero (School of Information)

Tuesday, 6/4/2019, 9:00am to 1:00pm.   ARCHIVED EVENT

Location: ISR Thompson 1430

The PDHP workshop series resumes with our first workshop of the summer: "Network Analysis: Overview and Applications To Population Science." Please join instructors Ceren Budak and Daniel Romero (both from U of M School of Information and formerly Microsoft Research) for a half-day workshop geared toward population researchers and data scientists of all experience levels.  The workshop features 2 hours of lecture (covering fundamental principles and theory of network analysis) followed by 2 hours of lab (simulation-based information diffusion within networks and optimal seed node selection), while exploring the connections between network analysis and social research.

Topics include:
- Basic concepts of networks and network data
- Measuring network properties such as centrality and node/edge importance
- Various models of information diffusion and cascade effects
- Network-based classification methods (including Random Walk and K-nearest neighbors)
- Network simulation using Python
- Impact of seed node selection on network properties.



The Population Dynamics and Health Program (PDHP) provides resources and services that support innovative approaches to data collection and analysis and the development of early-career population scientists, as well as research on significant and emergent issues in population dynamics and health.

BIO:

Ceren Budak interests lie in the area of computational social science. She is particularly interested in the use of large scale data sets and computational techniques to study problems with policy, social and political implications.

Daniel Romero's main research interest is the empirical and theoretical analysis of Social and Information Networks. He is particularly interested in understanding the mechanisms involved in network evolution, information diffusion, and user interactions on the Web.

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