Sequential sampling enhanced composite likelihood approach to estimation of social intercorrelations in large-scale networks

The increasing access to large social network data has generated substantial interest in the marketing community. However, due to its large scale, traditional analysis methods often become inadequate. In this paper, we propose a sequential sampling enhanced composite likelihood approach for efficient estimation of social intercorrelations in large-scale networks using the spatial model. Given a known population network, the proposed approach sequentially takes small samples from the network, and adaptively improves model parameter estimates through learnings obtained from previous samples. In comparison to population-based maximum likelihood estimation that is computationally prohibitive when the network size is large, the proposed approach makes it computationally feasible to analyze large networks and provide efficient estimation of social intercorrelations among members in large networks. In comparison to sample-based estimation that relies on information purely from the sample and produces underestimation bias in social intercorrelation estimates, the proposed approach effectively uses information from the population without compromising computation efficiency. Through simulation studies based on simulated networks and real networks, we demonstrate significant advantages of the proposed approach over benchmark estimation methods and discuss managerial implications. We also discuss extension of the proposed approach in the context of an unknown population network structure, as well as in an alternative form of the spatial model.