Abstract: We propose a model for the social flow of information in the form of text data, which simulates the
posting and sharing of short social media posts. Nodes in a graph representing a social network take
turns generating words, leading to a symbolic time series associated with each node. Information
propagates over the graph via a quoting mechanism, where nodes randomly copy short segments of
text from each other. We characterize information flows from these text via information-theoretic
estimators, and we derive analytic relationships between model parameters and the values of these
estimators. We explore and validate the model with simulations on small network motifs and larger
random graphs. Tractable models such as ours that generate symbolic data while controlling the
information flow allow us to test and compare measures of information flow applicable to real
social media data. In particular, by choosing different network structures, we can develop test
scenarios to determine whether or not measures of information flow can distinguish between true
and spurious interactions, and how topological network properties relate to information flow.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).