Mining the network value of customers
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Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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Personalized recommendation driven by information flow
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The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Information flow modeling based on diffusion rate for prediction and ranking
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Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Blogosphere: research issues, tools, and applications
ACM SIGKDD Explorations Newsletter
Ballot box communication in online communities
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Personalized recommendation based on the personal innovator degree
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Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Capturing implicit user influence in online social sharing
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Modeling Information Diffusion in Implicit Networks
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Social influence modeling on smartphone usage
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Information diffusion and external influence in networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Structure and dynamics of information pathways in online media
Proceedings of the sixth ACM international conference on Web search and data mining
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Many people share their activities with others through online communities. These shared activities have an impact on other users' activities. For example, users are likely to become interested in items that are adopted (e.g. liked, bought and shared) by their friends. In this paper, we propose a probabilistic model for discovering latent influence from sequences of item adoption events. An inhomogeneous Poisson process is used for modeling a sequence, in which adoption by a user triggers the subsequent adoption of the same item by other users. For modeling adoption of multiple items, we employ multiple inhomogeneous Poisson processes, which share parameters, such as influence for each user and relations between users. The proposed model can be used for finding influential users, discovering relations between users and predicting item popularity in the future. We present an efficient Bayesian inference procedure of the proposed model based on the stochastic EM algorithm. The effectiveness of the proposed model is demonstrated by using real data sets in a social bookmark sharing service.