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
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
Analyzing the Structure and Evolution of Massive Telecom Graphs
IEEE Transactions on Knowledge and Data Engineering
Maximizing a Submodular Set Function Subject to a Matroid Constraint (Extended Abstract)
IPCO '07 Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A note on maximizing the spread of influence in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Viral Marketing for Multiple Products
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
Sparsification of influence networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Tractable models for information diffusion in social networks
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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The well known influence maximization problem [1] (or viral marketing through social networks) deals with selecting a few influential initial seeds to maximize the awareness of product(s) over the social network. In this paper, we introduce a novel and generalized version of the influence maximization problem that considers simultaneously the following three practical aspects: (i) Often cross-sell among products is possible, (ii) Product specific costs (and benefits) for promoting the products have to be considered, and (iii) Since a company often has budget constraints, the initial seeds have to be chosen within a given budget. We refer to this generalized problem setting as Budgeted Influence Maximization with Cross-sell of Products (B-IMCP). To the best of our knowledge, we are not aware of any work in the literature that addresses the B-IMCP problem which is the subject matter of this paper. Given a fixed budget, one of the key issues associated with the B-IMCP problem is to choose the initial seeds within this budget not only for the individual products, but also for promoting cross-sell phenomenon among these products. In particular, the following are the specific contributions of this paper: (i) We propose an influence propagation model to capture both the cross-sell phenomenon and product specific costs and benefits; (ii) As the B-IMCP problem is NP-hard computationally, we present a simple greedy approximation algorithm and then derive the approximation guarantee of this greedy algorithm by drawing upon the results from the theory of matroids; (iii) We then outline two efficient heuristics based on well known concepts in the literature. Finally, we experimentally evaluate the proposed approach for the B-IMCP problem using a few well known social network data sets such as WikiVote data set, Epinions, and Telecom call detail records data.