The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Latent subject-centered modeling of collaborative tagging: An application in social search
ACM Transactions on Management Information Systems (TMIS)
Following the follower: detecting communities with common interests on twitter
Proceedings of the 23rd ACM conference on Hypertext and social media
Finding twitter communities with common interests using following links of celebrities
Proceedings of the 3rd international workshop on Modeling social media
The dynamic features of delicious, flickr, and YouTube
Journal of the American Society for Information Science and Technology
Tag-aware recommender systems: a state-of-the-art survey
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Towards Topic Trend Prediction on a Topic Evolution Model with Social Connection
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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Exploring community is fundamental for uncovering the connections between structure and function of complex networks and for practical applications in many disciplines such as biology and sociology. In this paper, we propose a TTR-LDA-Community model which combines the Latent Dirichlet Allocation model (LDA) and the Girvan-Newman community detection algorithm with an inference mechanism. The model is then applied to data from Delicious, a popular social tagging system, over the time period of 2005-2008. Our results show that 1) users in the same community tend to be interested in similar set of topics in all time periods; and 2) topics may divide into several sub-topics and scatter into different communities over time. We evaluate the effectiveness of our model and show that the TTR-LDA-Community model is meaningful for understanding communities and outperforms TTR-LDA and LDA models in tag prediction.