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
Word of Mouth: Rumor Dissemination in Social Networks
SIROCCO '08 Proceedings of the 15th international colloquium on Structural Information and Communication Complexity
Maximizing submodular set functions subject to multiple linear constraints
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Online stochastic packing applied to display ad allocation
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
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
Diffusion in social networks with competing products
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
Competing for customers in a social network: the quasi-linear case
WINE'06 Proceedings of the Second international conference on Internet and Network Economics
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Proceedings of the fifth ACM international conference on Web search and data mining
Competitive contagion in networks
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
A note on maximizing a submodular set function subject to a knapsack constraint
Operations Research Letters
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Motivated by applications to word-of-mouth advertising, we consider a game-theoretic scenario in which competing advertisers want to target initial adopters in a social network. Each advertiser wishes to maximize the resulting cascade of influence, modeled by a general network diffusion process. However, competition between products may adversely impact the rate of adoption for any given firm. The resulting framework gives rise to complex preferences that depend on the specifics of the stochastic diffusion model and the network topology. We study this model from the perspective of a central mechanism, such as a social networking platform, that can optimize seed placement as a service for the advertisers. We ask: given the reported demands of the competing firms, how should a mechanism choose seeds to maximize overall efficiency? Beyond the algorithmic problem, competition raises issues of strategic behaviour: rational agents should not be incentivized to underreport their budget demands. We show that when there are two players, the social welfare can be 2-approximated by a polynomial-time strategyproof mechanism. Our mechanism is defined recursively, randomizing the order in which advertisers are allocated seeds according to a particular greedy method. For three or more players, we demonstrate that under additional assumptions (satisfied by many existing models of influence spread) there exists a simpler strategyproof (e/e-1)-approximation mechanism; notably, this second mechanism is not necessarily strategyproof when there are only two players.