Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
A Potential-Based Node Selection Strategy for Influence Maximization in a Social Network
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Game theoretic network centrality: exact formulas and efficient algorithms
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Efficient computation of the shapley value for centrality in networks
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Study of diffusion models in an academic social network
ICDCIT'10 Proceedings of the 6th international conference on Distributed Computing and Internet Technology
A new approach to betweenness centrality based on the Shapley Value
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Efficient computation of the shapley value for game-theoretic network centrality
Journal of Artificial Intelligence Research
A game theory based approach for community detection in social networks
BNCOD'13 Proceedings of the 29th British National conference on Big Data
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In this paper, we consider the problem of selecting, for any given positive integer k, the top-k nodes in a social network, based on a certain measure appropriate for the social network. This problem is relevant in many settings such as analysis of co-authorship networks, diffusion of information, viral marketing, etc. However, in most situations, this problem turns out to be NP-hard. The existing approaches for solving this problem are based on approximation algorithms and assume that the objective function is sub-modular. In this paper, we propose a novel and intuitive algorithm based on the Shapley value, for efficiently computing an approximate solution to this problem. Our proposed algorithm does not use the sub-modularity of the underlying objective function and hence it is a general approach. We demonstrate the efficacy of the algorithm using a co-authorship data set from e-print arXiv (www.arxiv.org), having 8361 authors.