Link formation in cooperative situations
International Journal of Game Theory
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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The problem of finding the influencers in social networks has been traditionally dealt in an optimization setting by finding the top-k nodes that has the maximum information spread in the network. These methods aim to find the influencers in a network through the process of information diffusion. However, none of these approaches model the individual social value generated by collaborations in these networks. Such social value is often the real motivation for which the nodes connect to each other. In this work, we propose a framework to compute this network social value using the concept of social capital, namely the amount of bonding and bridging connections in the network. We first compute the social capital value of the network and then allocate this network value to the nodes of the network. We establish the fairness of our allocation using several axioms of fairness. Our experiments on the real data sets show that the computed social capital is an excellent proxy for finding influencers and our approach outperforms several popular baselines.