Network Influences on Behavior: A Summary of Tom Valente's Keynote Address at Sunbelt XXXV: The Annual Meeting of the International Network for Social Network Analysis.

Tom Valente’s 2015 keynote address overviewed his career focused on network models of the diffusion of innovations and behavior change, where he made his mark as a skilled theoretician. He is well known in the academic community as a willing collaborator and networker. He has made singular contributions to network models of the diffusion of innovations, including the role of opinion leaders, and network interventions to promote behavior change. Tom’s keynote featured empirical findings from applying his theoretical models to classic diffusion datasets and current work focused on the diffusion of global tobacco policy. He concluded his talk with a summary of network interventions, which may be used to guide intervention development, evaluation, and dissemination (Valente, 2012; Valente, Palinkas, Czaja, Chu, & Brown, 2015). His keynote address emphasized not only his scientific contributions but also how his career was guided and influenced by colleagues, friends, and mentors. Tom’s work highlights the need to examine personal network exposure and thresholds in addition to exposure from the whole network when assessing behavior, behavior change, and intervention effects. Diffusion of innovation theory explains how ideas, behaviors, and products spread throughout a network (Valente & Rogers, 1995). Tom expanded upon diffusion theory for his dissertation by providing theory and techniques for integrating threshold and critical mass models with the diffusion process (Valente, 1995). Tom’s network threshold model differed from Granovetter’s (1983) threshold model in that Granovetter’s model was predicated on people’s innovativeness relative to the whole system, whereas Tom calculated thresholds relative to an individual’s personal network. The novelty of Tom’s dissertation was that some people are innovative relative to the whole community, but late adopters relative to their personal network and vice versa. A person’s position in the network determines their exposure and people can be late adopters because their network position is such that they learn about the innovation late. In order to complete a dissertation on network diffusion, Tom needed data. He realized that he needed to acquire secondary data to analyze as diffusion data can take years to collect since diffusion takes a long time. At this point in time (1989), few network diffusion studies had been conducted and of these some were lost. Of the studies he identified, data from three of them could be obtained and these became the three classic diffusion network datasets: Medical Innovation (Coleman, Katz, & Menzel, 1966), Brazilian Farmers (Rogers, Ascroft, & Röling, 1970), and Korean Family Planning (Rogers & Kincaid, 1981). These three datasets have been