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Humans communicate, receive, and store information using sequences of items-- from words in a sentence or notes in music to abstract concepts in lecturesand books. The networks formed by these items (nodes) and the sequentialtransitions between them (edges) encode important structural features of humancommunication and knowledge. But how do humans learn the networks ofprobabilistic transitions that underlie sequences of items? Moreover, what dopeople's internal maps of these networks look like? Here, we introduce graphlearning, a growing and interdisciplinary field focused on studying how humanslearn and represent networks in the world around them. We begin by describingestablished results from statistical learning showing that humans are adept atdetecting differences in the transition probabilities between items in asequence. We next present recent experiments that directly control fordifferences in transition probabilities, demonstrating that human behavior alsodepends critically on the abstract network structure of transitions. Finally,we present computational models that researchers have proposed to explain theeffects of network structure on human behavior and cognition. Throughout, wehighlight a number of exciting open questions in the study of graph learningthat will require creative insights from cognitive scientists and networkscientists alike.

How humans learn and represent networks