Reviewed Version (pdf): https://openreview.net/references/pdf?id=IlLuYHWSW
Keywords: matrix factorization, honey bees, explainable, social networks, implicit bias, dataset
Abstract: Honey bees are a popular model for complex social systems, in which global behavior emerges from the actions and interactions of thousands of individuals. While the average life of a bee is organized as a sequence of tasks roughly determined by age, there is substantial variation at the individual level. For example, young bees can become foragers early in life, depending on the colony’s needs. Using a unique dataset containing lifetime trajectories of all individuals over multiple generations in two honey bee colonies, we propose a new temporal matrix factorization model that jointly learns the average developmental path and structured variations of individuals in the social network over their entire lives. Our method yields inherently interpretable embeddings that are biologically plausible and consistent over time, which allow one to compare individuals regardless of when, or in which colony, they lived. Our method provides a quantitative framework for understanding behavioral heterogeneity in complex social systems applicable in fields such as behavioral biology, social sciences, neuroscience, and information science.
One-sentence Summary: Factorizing social network of honey bees using a novel temporal NMF algorithm.
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