Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Prediction of Information Diffusion Probabilities for Independent Cascade Model
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient estimation of influence functions for SIS model on social networks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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
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
Capturing implicit user influence in online social sharing
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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In this paper we propose models for inferring a social network of smartphone users. By applying the concept of information diffusion models to the log of application executions in smartphones, strength of relationships among users will be estimated as an optimization problem. Functions on time difference and application significance are employed to capture user behavior precisely. In addition, affiliation information of users is effectively utilized as an exogenous factor. Experimental results using 157 of smartphone users indicate that the proposed model outperforms naive methods and infers a social network appropriately. Especially, the model succeeds in capturing the important relations in user communities accurately.