Finding influencers in networks using social capital

The existing methods for finding influencers use the process of information diffusion to discover the nodes with the maximum expected information spread. These models capture only the process of information diffusion and not the actual social value of collaborations in the network. We have proposed a method for finding influencers based on the notion that people generate more value for their work by collaborating with peers of high influence. The social value generated through such collaborations denotes the individual social capital. We hypothesize and show that players with high individual social capital are often key influencers in the network. We propose a value-allocation model to compute the social capital and allocate the fair share of this capital to each individual involved in the collaboration. We show that our allocation satisfies several axioms of fairness and falls in the same class as the Myerson’s allocation function. We implement our allocation rule using an efficient algorithm SoCap and show that our algorithm outperforms the baselines in several real-life data sets. Specifically, in DBLP network, our algorithm outperforms PageRank, PMIA and Weighted Degree baselines up to 8 % in terms of precision, recall and $$F_1$$F1-measure.