Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
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
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
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
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
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
A Generalized Linear Threshold Model for Multiple Cascades
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Threshold models for competitive influence in social networks
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Limiting the spread of misinformation in social networks
Proceedings of the 20th international conference on World wide web
A data-based approach to social influence maximization
Proceedings of the VLDB Endowment
SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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The key algorithmic problem in viral marketing is to identify a set of influential users (called seeds) in a social network, who, when convinced to adopt a product, shall influence other users in the network, leading to a large number of adoptions. When two or more players compete with similar products on the same network we talk about competitive viral marketing, which so far has been studied exclusively from the perspective of one of the competing players. In this paper we propose and study the novel problem of competitive viral marketing from the perspective of the host, i.e., the owner of the social network platform. The host sells viral marketing campaigns as a service to its customers, keeping control of the selection of seeds. Each company specifies its budget and the host allocates the seeds accordingly. From the host's perspective, it is important not only to choose the seeds to maximize the collective expected spread, but also to assign seeds to companies so that it guarantees the "bang for the buck" for all companies is nearly identical, which we formalize as the fair seed allocation problem. We propose a new propagation model capturing the competitive nature of viral marketing. Our model is intuitive and retains the desired properties of monotonicity and submodularity. We show that the fair seed allocation problem is NP-hard, and develop an efficient algorithm called Needy Greedy. We run experiments on three real-world social networks, showing that our algorithm is effective and scalable.