Network Effects & Personal Influences

Wat ouder (Augustus 2010) maar interessant onderzoek over diffusie in sociale netwerken. Een interessante bevinding die tegen het gevoel ingaat is dat de gemiddelde beinvloedingskracht van een individu vermindert als het totaal aantal contacten toeneemt.


We study the diffusion process in an online social network given the individual connections

between members. We model the adoption decision of individuals as a binary choice affected by

three factors: (1) the local network structure formed by already adopted neighbors, (2) the

average characteristics of adopted neighbors (influencers), and (3) the characteristics of the

potential adopters. Focusing on the first factor, we find two marked effects. First, an individual

who is connected to many adopters has a higher adoption probability (degree effect). Second, the

density of connections in a group of already adopted consumers has a strong positive effect on the

adoption of individuals connected to this group (clustering effect). We also record significant

effects for influencer and adopter characteristics. Specifically, for adopters, we find that their

position in the entire network and some demographic variables are good predictors of adoption.

Similarly, in the case of already adopted individuals, average demographics and global network

position can predict their influential power on their neighbors. An interesting counter-intuitive

finding is that the average influential power of individuals decreases with the total number of their

contacts. These results have practical implications for viral marketing in a context where,

increasingly, a variety of technology platforms are considering to leverage their consumers’

revealed connection patterns. In particular, our model performs well in predicting the next set of



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