Link formation in cooperative situations
International Journal of Game Theory
Introduction to algorithms
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Strategic network formation with structural holes
Proceedings of the 9th ACM conference on Electronic commerce
Social capital in online communities
Proceedings of the 2nd PhD workshop on Information and knowledge management
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Implicit affinity networks and social capital
Information Technology and Management
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
Tractable models for information diffusion in social networks
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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The existing methods for finding influencers use the process of information diffusion to discover the nodes with maximum 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 using the idea that people generate more value for their work by collaborating with peers of high influence. The social value generated through such collaborations denotes the notion of individual social capital. We hypothesize and show that players with high 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 F1-measure.