This talk explains how agents can learn from dispersed information and solve inference tasks of varying degrees of complexity through localized processing. The presentation also shows how information/misinformation is diffused over graphs, how beliefs are formed, and how the graph topology helps resist/enable manipulation. Examples will be considered in the context of social learning, teamwork, distributed optimization, and adversarial behavior.