Approximations for the maximum acyclic subgraph problem
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Approximation alogorithms for the maximum acyclic subgraph problem
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
Mining the network value of customers
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Maximizing the spread of influence through a social network
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Group formation in large social networks: membership, growth, and evolution
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Maximizing Non-Monotone Submodular Functions
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Mining social networks using heat diffusion processes for marketing candidates selection
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Discovering leaders from community actions
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A measurement-driven analysis of information propagation in the flickr social network
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Community gravity: measuring bidirectional effects by trust and rating on online social networks
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Non-monotone submodular maximization under matroid and knapsack constraints
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The price of anarchy in bertrand games
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Blog cascade affinity: analysis and prediction
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Pricing Strategies for Viral Marketing on Social Networks
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Nudging Mechanisms for Technology Adoption
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Revenue maximization with a single sample
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Social Network Analysis and Mining for Business Applications
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Maximizing product adoption in social networks
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Proceedings of the fifth ACM international conference on Web search and data mining
On allocations with negative externalities
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Optimal pricing in social networks with incomplete information
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How to influence people with partial incentives
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We discuss the use of social networks in implementing viral marketing strategies. While influence maximization has been studied in this context (see Chapter 24 of [10]), we study revenue maximization, arguably, a more natural objective. In our model, a buyer's decision to buy an item is influenced by the set of other buyers that own the item and the price at which the item is offered. We focus on algorithmic question of finding revenue maximizing marketing strategies. When the buyers are completely symmetric, we can find the optimal marketing strategy in polynomial time. In the general case, motivated by hardness results, we investigate approximation algorithms for this problem. We identify a family of strategies called influence-and-exploit strategies that are based on the following idea: Initially influence the population by giving the item for free to carefully a chosen set of buyers. Then extract revenue from the remaining buyers using a 'greedy' pricing strategy. We first argue why such strategies are reasonable and then show how to use recently developed set-function maximization techniques to find the right set of buyers to influence.