Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth 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
Ant colony optimization theory: a survey
Theoretical Computer Science
Maximizing influence in a competitive social network: a follower's perspective
Proceedings of the ninth international conference on Electronic commerce
Word of Mouth: Rumor Dissemination in Social Networks
SIROCCO '08 Proceedings of the 15th international colloquium on Structural Information and Communication Complexity
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
Submodularity of Influence in Social Networks: From Local to Global
SIAM Journal on Computing
Threshold models for competitive influence in social networks
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Maximizing influence spread in modular social networks by optimal resource allocation
Expert Systems with Applications: An International Journal
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
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Consumers often form complex social networks based on a multitude of different relations and interactions. By virtue of these interactions, they influence each other's decisions in adopting products or behaviors. Therefore, it is essential for companies to identify influential consumers to target, in the hopes that influencing them will lead to a large cascade of further recommendations. Several studies, based on approximation algorithms and assume that the objective function is monotonic and submodular, have been addressed this issue of viral marketing. However, there is a complex and broad family of diffusion models in competitive environment, and the properties of monotonic and submodular may not be upheld. Therefore, in this research, we borrowed from swarm intelligence-specifically the ant colony optimization algorithm-to address the competitive influence-maximization problem. The proposed approaches were evaluated using a coauthorship data set from the arXiv e-print (http://www.arxiv.org), and the obtained experimental results demonstrated that our approaches outperform two well-known benchmark heuristics.