Short Course Catalog

Temporal Networks: The Dynamics of Social Contagion

Who Should Attend

The target audience for the course Temporal Networks: The Dynamics of Social Contagion includes corporate executives, organizational consultants, marketing professionals, social media app developers, emergency managers, and public health professionals.

Course Description

Information is shared among members of a group according to established patterns of social organization. In the form of knowledge or positive behaviors, such sharing may build equity, enhance access, and overcome obstacles to innovation and discovery. In the form of infection or negative behaviors, such sharing may expose the group to collective malfunction and disaster. Workplaces, family units, and industry networks are examples of human groups where understanding the nature of information transmission is vital to ensuring harmony and function.

The course Temporal Networks: The Dynamics of Social Contagion explores mathematical models of social organization inspired by the dynamics of insect societies, and the ways in which information transmission produces observed behaviors. Emphasis is on transmission over time, ordered by a sequence of interactions between individual members of the society.

The course aims to build working familiarity with the terminology and tools used by engineers and scientists to characterize such temporal networks. This is accomplished through hands-on learning experiences built around the computational analysis of publicly-available network data sets from research on human and insect societies, highlighting similarities and differences. Participants’ engagement with models of human group dynamics further explore principles of self-organization and its possible outcomes.

Learning Outcomes

The course Temporal Networks: The Dynamics of Social Contagion, aims to enable participants to

• Recognize properties of information transmission in social networks and give examples of beneficial and detrimental behaviors that may result from such transmission.
• Understand how to use computer simulations to explore datasets of time-dependent interactions in social organizations.
• Evaluate designs for collection of time-dependent interaction data in terms of temporal and behavioral resolution.

Computer and/or Software Requirements

Participants should bring a laptop computer.

Resources and References

• Gernat, T., Rao, V.D., Middendorf, M., Dankowicz, H., Goldenfeld, N., Robinson, G.E., “Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks.” https://www.pnas.org/content/115/7/1433
• Saghafi, M., Dankowicz, H., Tabor, W., “Emergent Task Differentiation on Network Filters”: https://epubs.siam.org/doi/pdf/10.1137/16M1084432
• Li, M., Rao, V.D., Gernat, T., Dankowicz, H., “Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks.” https://www.nature.com/articles/s41598-017-18450-3

Sample Course Outline

Day 1 – Data sets and models

Day 2 – Information transmission 

Day 3 – Social networks

About the Instructor

Harry Dankowicz is professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign. He earned his Master of Science in Engineering Physics from KTH Royal Institute of Technology in Stockholm, Sweden in 1991 and his PhD in Theoretical and Applied Mechanics from Cornell University in 1995. He has held faculty positions at Virginia Polytechnic Institute and State University and, since 2005, the University of Illinois at Urbana-Champaign. Prof. Dankowicz is a recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) from the US National Science Foundation and the Archie Higdon Distinguished Educator Award from the American Society for Engineering Education. He served as principal investigator on NSF-funded multidisciplinary research to analyze emergent task-differentiation in insect societies and human coordination games and has published extensively on the modeling and analysis of nonlinear dynamics and control.