The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Dynamic mixed membership blockmodel for evolving networks
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
Journal of the ACM (JACM)
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Analysis of large multi-modal social networks: patterns and a generator
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Multiple domain user personalization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Overlapping communities in dynamic networks: their detection and mobile applications
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
ComSoc: adaptive transfer of user behaviors over composite social network
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Modeling the dynamics of online social networks over time not only helps us understand the evolution of network structures and user behaviors, but also improves the performance of other analysis tasks, such as link prediction and community detection. Nowadays, users engage in multiple networks and form a "composite social network" by considering common users as the bridge. State-of-the-art network-dynamics analysis is performed in isolation for individual networks, but users' interactions in one network can influence their behaviors in other networks, and in an individual network, different types of user interactions also affect each other. Without considering the influences across networks, one may not be able to model the dynamics in a given network correctly due to the lack of information. In this paper, we study the problem of modeling the dynamics of composite networks, where the evolution processes of different networks are jointly considered. However, due to the difference in network properties, simply merging multiple networks into a single one is not ideal because individual evolution patterns may be ignored and network differences may bring negative impacts. The proposed solution is a nonparametric Bayesian model, which models each user's common latent features to extract the cross-network influences, and use network-specific factors to describe different networks' evolution patterns. Empirical studies on large-scale dynamic composite social networks demonstrate that the proposed approach improves the performance of link prediction over several state-of-the-art baselines and unfolds the network evolution accurately.