Mining the network value of customers
Proceedings of the seventh 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
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Randomization tests for distinguishing social influence and homophily effects
Proceedings of the 19th international conference on World wide web
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Influence and passivity in social media
Proceedings of the 20th international conference companion 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
RecMax: exploiting recommender systems for fun and profit
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 21st ACM international conference on Information and knowledge management
Finding influential products on social domination game
Proceedings of the 21st ACM international conference on Information and knowledge management
Personalized influence maximization on social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A probability based algorithm for influence maximization in social networks
Proceedings of the 5th Asia-Pacific Symposium on Internetware
Mobility and social networking: a data management perspective
Proceedings of the VLDB Endowment
Phase transition of multi-state diffusion process in networks
ACM SIGMETRICS Performance Evaluation Review
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One of the key objectives of viral marketing is to identify a small set of users in a social network, who when convinced to adopt a product will influence others in the network leading to a large number of adoptions in an expected sense. The seminal work of Kempe et al. [13] approaches this as the problem of influence maximization. This and other previous papers tacitly assume that a user who is influenced (or, informed) about a product necessarily adopts the product and encourages her friends to adopt it. However, an influenced user may not adopt the product herself, and yet form an opinion based on the experiences of her friends, and share this opinion with others. Furthermore, a user who adopts the product may not like the product and hence not encourage her friends to adopt it to the same extent as another user who adopted and liked the product. This is independent of the extent to which those friends are influenced by her. Previous works do not account for these phenomena. We argue that it is important to distinguish product adoption from influence. We propose a model that factors in a user's experience (or projected experience) with a product. We adapt the classical Linear Threshold (LT) propagation model by defining an objective function that explicitly captures product adoption, as opposed to influence. We show that under our model, adoption maximization is NP-hard and the objective function is monotone and submodular, thus admitting an approximation algorithm. We perform experiments on three real popular social networks and show that our model is able to distinguish between influence and adoption, and predict product adoption much more accurately than approaches based on the classical LT model.