Adversarial models in evolutionary game dynamics
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
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
Maximizing influence in a competitive social network: a follower's perspective
Proceedings of the ninth international conference on Electronic commerce
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding spread blockers in dynamic networks
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Sparsification of influence networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
CASINO: towards conformity-aware social influence analysis in online social networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Maximizing product adoption in social networks
Proceedings of the fifth ACM international conference on Web search and data mining
StaticGreedy: solving the scalability-accuracy dilemma in influence maximization
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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It has been observed that individuals' decisions to adopt a product or innovation are often influenced by the recommendations of their friends and acquaintances. Motivated by this observation, the last few years have seen a number of studies on influence maximization in social networks. The primary goal of these studies is identification of k most influential nodes in a network. A major limitation of these studies is that they focus on a non-adversarial environment, where only one player is engaged in influencing the nodes. However, in a realistic scenario multiple players attempt to influence the nodes in a competitive fashion. The proposed model considers a competitive environment where a node that has not yet adopted an innovation, can adopt only one of the several competing innovations and once it adopts an innovation, it does not switch. The paper studies the scenario where the first player has already chosen a set of k nodes and the second player, with the knowledge of the choice of the first, attempts to identify a smallest set of nodes (excluding the ones already chosen by the first) so that when the influence propagation process ends, the number of nodes influenced by the second player is larger than the number of nodes influenced by the first. The paper studies two propagation models and shows that in both the models, the identification of the smallest set of nodes to defeat the adversary is NP-Hard. It provides an approximation algorithm and proves that the performance bound is tight. It also presents the results of extensive experimentation using the collaboration network data. Experimental results show that the second player can easily defeat the first with this algorithm, if the first utilizes the node degree or closeness centrality based algorithms for the selection of influential nodes. The proposed algorithm also provides better performance if the second player utilizes it instead of the greedy algorithm to maximize its influence.