Gossip: Identifying Central Individuals in Networks and Information Diffusion Processes

How can we identify the most influential nodes in a network for initiating diffusion? Are people able to easily identify those people in their communities who are best at spreading information, and if so How?    Using theory and recent data, we examine these questions and see how the structure of social networks affects information transmission ranging from gossip to the diffusion of new products.   In particular, a modelof diffusion is used to define centrality and shown to nest other measures of centrality as extreme special cases.  Then it will be shown that by tracking gossip within a network, nodes can easily learn to rank the centrality of other nodes without knowing anything about the network itself.  The theoretical predictions are consistent with new field experiments. 

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