Spanning trees with many leaves
SIAM Journal on Discrete Mathematics
Approximating maximum leaf spanning trees in almost linear time
Journal of Algorithms
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
2-Approximation Algorithm for Finding a Spanning Tree with Maximum Number of Leaves
ESA '98 Proceedings of the 6th Annual European Symposium on Algorithms
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
How much is your personal recommendation worth?
Proceedings of the 19th international conference on World wide web
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
Mechanisms for multi-level marketing
Proceedings of the 12th ACM conference on Electronic commerce
Proceedings of the fifth ACM international conference on Web search and data mining
On allocations with negative externalities
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Optimal pricing in social networks with incomplete information
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Simpler sybil-proof mechanisms for multi-level marketing
Proceedings of the 13th ACM Conference on Electronic Commerce
Optimal incentive timing strategies for product marketing on social networks
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Maximizing revenue from strategic recommendations under decaying trust
Proceedings of the 21st ACM international conference on Information and knowledge management
On modeling product advertisement in large-scale online social networks
IEEE/ACM Transactions on Networking (TON)
WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
Computing a profit-maximizing sequence of offers to agents in a social network
WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
Pricing public goods for private sale
Proceedings of the fourteenth ACM conference on Electronic commerce
Competitive auctions for markets with positive externalities
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
How to influence people with partial incentives
Proceedings of the 23rd international conference on World wide web
Hi-index | 0.00 |
We study the use of viral marketing strategies on social networks that seek to maximize revenue from the sale of a single product. We propose a model in which the decision of a buyer to buy the product is influenced by friends that own the product and the price at which the product is offered. The influence model we analyze is quite general, naturally extending both the Linear Threshold model and the Independent Cascade model, while also incorporating price information. We consider sales proceeding in a cascading manner through the network, i.e. a buyer is offered the product via recommendations from its neighbors who own the product. In this setting, the seller influences events by offering a cashback to recommenders and by setting prices (via coupons or discounts) for each buyer in the social network. This choice of prices for the buyers is termed as the seller's strategy.Finding a seller strategy which maximizes the expected revenue in this setting turns out to be NP-hard. However, we propose a seller strategy that generates revenue guaranteed to be within a constant factor of the optimal strategy in a wide variety of models. The strategy is based on an influence-and-exploit idea, and it consists of finding the right trade-off at each time step between: generating revenue from the current user versus offering the product for free and using the influence generated from this sale later in the process.