Designing Computational Tools for Analyzing Dynamic Social Networks in Population Biology

1. Computational Tools for Population Biology Tanya Berger-Wolf, Computer Science, UIC; Daniel Rubenstein, Ecology and Evolutionary Biology, Princeton; Jared Saia, Computer Science, U New Mexico • Recent breakthroughs in data collection technology, such as GPS and other mobile sensors, are giving biologists access to data about social interactions of wild populations on a scale never seen before. Such data offer the promise of answering some of the big questions in population biology. • Unfortunately, in this domain, our ability to analyze data lags substantially behind our ability to collect it. Particularly, current methods for analysis of social interactions are mostly static. • Our goal is to design a computational framework for analysis of dynamic social networks and validate it by applying to equid populations (zebras, horses, onagers). • Collect explicitly dynamic social data: sensor collars on animals, synthetic population simulations, cellphone and email communications, … • Represent a time series of observation snapshots as a series of networks. Use machine learning, data mining, and algorithm design techniques to identify critical individuals, communities, and patterns in dynamic networks. • Validate theoretical predictions derived from the abstract graph representation by simulations on collected data and controlled and quazi-experiments on real populations • Done: • Formal computational framework for analysis of dynamic social networks • Scalable methods for • dentifyingdynamic communities • identifying periodic patterns • predicting part of network structure • identifying individuals critical for initiating and blocking spreading processes • Future: • Validate methods on biological data • Extend methods from networks of unique individuals to classes of individuals