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Modelling Innovation Adoption Spreading in Complex Networks

Authors: Duanmu, J.-L. and Chai, W.K.

Journal: Applied Network Science

Publisher: SpringerOpen

eISSN: 2364-8228

ISSN: 2364-8228

Abstract:

Innovation adoption pattern has been found to be influenced by the underly- ing social network structure and its constituent entities. In this paper, we model innovation diffusion considering (1) the role of network structures in dictating the spread of adoption and (2) how individual’s characteristic/capability influences the path of diffusion (e.g. an individual may have different attitude or ability towards adopting a new innovation). We consider that each individual is unique and his/her position in the network is important. We draw on the epidemic the- ory and model the diffusion dynamics via a continuous-time Markov chain which offers strong analytical tractability while retaining a high-level of generality. Our model allows derivation of individual’s adoption probability and the aggregate adoption behavior of the network as a whole. Precise computation of individual adoption decision conditioned by the population’s behavior is of exponential com- plexity (i.e., the state space exponentially increases with the size of the network). By applying a mean field approximation, the analysis complexity of the spreading mechanics is reduced from exponential (O(5^N)) to polynomial (O(N )) and thus allowing our approach to scale for large networks. We offer insights into how the network spectrum affects the innovation exposure rate and spreading of innova- tion individually and across communities with different adoption behaviors. We compare our model against a wide-range of Monte-Carlo experiments and show close agreements in different settings (including both homogeneous and heteroge- neous population cases). Finally, we illustrate the effects of the embedded social structure and the characteristics of individuals in the network on the path of innovation diffusion via two use cases: (i) innovation adoption of EU countries in a Single Market Programme and (ii) innovation adoption of specific class of technology (specifically financial technologies (FinTech)).

https://eprints.bournemouth.ac.uk/40795/

Source: Manual

Modelling Innovation Adoption Spreading in Complex Networks

Authors: Duanmu, J.-L. and Chai, W.K.

Journal: Applied Network Science

Publisher: SpringerOpen

ISSN: 2364-8228

Abstract:

Innovation adoption pattern has been found to be influenced by the underly- ing social network structure and its constituent entities. In this paper, we model innovation diffusion considering (1) the role of network structures in dictating the spread of adoption and (2) how individual’s characteristic/capability influences the path of diffusion (e.g. an individual may have different attitude or ability towards adopting a new innovation). We consider that each individual is unique and his/her position in the network is important. We draw on the epidemic the- ory and model the diffusion dynamics via a continuous-time Markov chain which offers strong analytical tractability while retaining a high-level of generality. Our model allows derivation of individual’s adoption probability and the aggregate adoption behavior of the network as a whole. Precise computation of individual adoption decision conditioned by the population’s behavior is of exponential com- plexity (i.e., the state space exponentially increases with the size of the network). By applying a mean field approximation, the analysis complexity of the spreading mechanics is reduced from exponential (O(5^N)) to polynomial (O(N )) and thus allowing our approach to scale for large networks. We offer insights into how the network spectrum affects the innovation exposure rate and spreading of innova- tion individually and across communities with different adoption behaviors. We compare our model against a wide-range of Monte-Carlo experiments and show close agreements in different settings (including both homogeneous and heteroge- neous population cases). Finally, we illustrate the effects of the embedded social structure and the characteristics of individuals in the network on the path of innovation diffusion via two use cases: (i) innovation adoption of EU countries in a Single Market Programme and (ii) innovation adoption of specific class of technology (specifically financial technologies (FinTech)).

https://eprints.bournemouth.ac.uk/40795/

Source: BURO EPrints