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Abstract

From social networks to public transportation, graph structuresare a ubiquitous feature of life. How do humans learn functionson graphs, where relationships are defined by the connectiv-ity structure? We adapt a Bayesian framework for functionlearning to graph structures, and propose that people performgeneralization by assuming that the observed function valuesdiffuse across the graph. We evaluate this model by askingparticipants to make predictions about passenger volume in avirtual subway network. The model captures both generaliza-tion and confidence judgments, and provides a quantitativelysuperior account relative to several heuristic models. Our worksuggests that people exploit graph structure to make general-izations about functions in complex discrete spaces.