Centrality, gossip, and diffusion of information in networks

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 will 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, the concept of diffusion centrality from Banerjee, Chandrasekhar, Duflo, and Jackson (2013) will be considered and shown to nest degree centrality, eigenvector centrality, and 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. Finally, the theoretical predictions will be tested with data. The results are presented in Banerjee, Chandrasekhar, Duflo, and Jackson (2014).