Spatial Diffusion of Innovation

New products, services and technologies typically stem from an original location and diffuse to other places in case they are successful. We study how innovation spreads across space and through social networks.

Related Publications

How is online social media activity structured in the geographical space? Recent studies have shown that in spite of earlier visions about the “death of distance”, physical proximity is still a major factor in social tie formation and maintenance in virtual social networks. Yet, it is unclear, what are the characteristics of the distance dependence in online social networks. In order to explore this issue the complete network of the former major Hungarian online social network is analyzed. We find that the distance dependence is weaker for the online social network ties than what was found earlier for phone communication networks. For a further analysis we introduced a coarser granularity: We identified the settlements with the nodes of a network and assigned two kinds of weights to the links between them. When the weights are proportional to the number of contacts we observed weakly formed, but spatially based modules resemble to the borders of macro-regions, the highest level of regional administration in the country. If the weights are defined relative to an uncorrelated null model, the next level of administrative regions, counties are reflected.

Online social networks (OSN) are major platforms of ICT‐enabled communication, supporting place‐independent social life. However, recent findings suggest that the geographical location of users strongly affects network topology. Therefore, OSNs may be simultaneously related to locations and also unlocked from offline geographies. Our paper addresses this dual‐faced phenomenon, analysing the location‐specific effect on OSN diffusion and OSN usage. Findings on iWiW (International Who Is Who), the leading OSN in Hungary in the 2000s with more than 4 million users, suggest that the rate of users (proxy for OSN diffusion) is positively associated with the geographical proximity of Budapest, the foremost urban centre in the country. On the contrary, the average number of connections (proxy for OSN usage) is independent of the geographical proximity of the capital, and it is even higher in peripheral regions when controlling for other offline factors.

The urban–rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. Although both model versions can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales in all model specifications.