Abstract
Ties between individuals on a social network can represent different dimensions of interactions, and the spreading of information and innovations on these networks could potentially be driven by some dimensions more than by others. In this paper we investigate this issue by studying the diffusion of microfinance within rural India villages and accounting for the whole multilayer structure of the underlying social networks. We define a new measure of node centrality, diffusion versatility, and show that this is a better predictor of microfinance participation rate than previously introduced measures defined on aggregated single-layer social networks. Moreover, we untangle the role played by each social dimension and find that the most prominent role is played by the nodes that are central on layers concerned with trust, shedding new light on the key triggers of the diffusion of microfinance.
Highlights
Understanding the mechanisms driving the diffusion of information, behaviours, and innovations is a question of great interest for social and economical sciences (Bond et al 2012; Coleman et al 1957; Rogers 1962)
Comparing centrality and versatility node rankings First, we show that ranking nodes according to their diffusion versatility is significantly different than ranking them according to their diffusion centrality in the aggregated network
96% of the nodes do not occupy the same position in the two rankings, and 28% of them present a rank difference greater than or equal to 10. This result suggests that diffusion versatility provides different information with respect to diffusion centrality, and we explore whether this information can lead to a better prediction of microfinance participation, and, more importantly, to the detection of which kinds of tie play the most important role
Summary
Understanding the mechanisms driving the diffusion of information, behaviours, and innovations is a question of great interest for social and economical sciences (Bond et al 2012; Coleman et al 1957; Rogers 1962). The role played by the social structure of the system has since been widely investigated using the mathematical formalism of networks (Valente 1995; Watts 2002), and a fundamental question has been the identification of the most influential individuals therein (Freeman 1979; Kitsak et al 2010). This is especially important in the context of network interventions, which is concerned with understanding how social networks influence behaviours and their diffusions (Valente 2012). Studies have shown that their success is critically dependent on the choice of influencers (Valente and Davis 1999) and on their position in the network (Aral et al 2013)
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