The proliferation of online social networks enables the influence of a person or an event to propagate to every corner of the globe in a very short duration of time. The problem of identifying such key sources of influence is important for a wide variety of applications from sales and marketing to public health and policies. Most of the existing methods for identifying influencers use the process of information diffusion to discover the nodes (people) with the maximum expected information spread. In this work we have developed a novel method for identifying key influencers in a given network. This method works on the premise that people generate more value for their work by collaborating with peers having high influence. The social value generated through such collaborations denotes the notion of individual social capital. At the core of this method we use the popular valuation-allocation approach for finding the individual social capital value. In this approach first we determine the value of the entire network using a valuation function and then we do a fair allocation of this entire network's value amongst the participating nodes (people). We show that our Valuation and Allocation functions satisfy several axioms of fairness and fall under the Myerson's allocation rule class.Also, we implement our allocation rule using an efficient algorithm and show that our algorithm outperforms the baselines in several real life datasets. Especially, for the DBLP collaboration network our algorithm outperforms PageRank, PMIA and Weighted Degree baselines by up to 8% in terms of precision recall and F1-measure.Furthermore, we use Hypergraphs as a tool to model group collaborations more effectively and empirically show the superiority of hypergraph edge weights as compared to dyadic edge weights for identifying influencers.To conclude with we discuss a couple of popular distributed programming paradigms, namely MapReduce and BSP (Bulk Synchronous Parallel) and the implementation of the algorithm on these.