Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Mining advisor-advisee relationships from research publication networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Outtweeting the twitterers - predicting information cascades in microblogs
WOSN'10 Proceedings of the 3rd conference on Online social networks
Predicting popular messages in Twitter
Proceedings of the 20th international conference companion on World wide web
Information Sciences: an International Journal
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Multi-relational Link Prediction in Heterogeneous Information Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Who will follow you back?: reciprocal relationship prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Assessing the Quality of Diffusion Models Using Real-World Social Network Data
TAAI '11 Proceedings of the 2011 International Conference on Technologies and Applications of Artificial Intelligence
Inferring social ties across heterogenous networks
Proceedings of the fifth ACM international conference on Web search and data mining
Cross-lingual knowledge linking across wiki knowledge bases
Proceedings of the 21st international conference on World Wide Web
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Exploiting latent information to predict diffusions of novel topics on social networks
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Link Prediction: Fair and Effective Evaluation
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Link Prediction and Recommendation across Heterogeneous Social Networks
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Personalized entity recommendation: a heterogeneous information network approach
Proceedings of the 7th ACM international conference on Web search and data mining
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The concern of privacy has become an important issue for online social networks. In services such as Foursquare.com, whether a person likes an article is considered private and therefore not disclosed; only the aggregative statistics of articles (i.e., how many people like this article) is revealed. This paper tries to answer a question: can we predict the opinion holder in a heterogeneous social network without any labeled data? This question can be generalized to a link prediction with aggregative statistics problem. This paper devises a novel unsupervised framework to solve this problem, including two main components: (1) a three-layer factor graph model and three types of potential functions; (2) a ranked-margin learning and inference algorithm. Finally, we evaluate our method on four diverse prediction scenarios using four datasets: preference (Foursquare), repost (Twitter), response (Plurk), and citation (DBLP). We further exploit nine unsupervised models to solve this problem as baselines. Our approach not only wins out in all scenarios, but on the average achieves 9.90% AUC and 12.59% NDCG improvement over the best competitors. The resources are available at http://www.csie.ntu.edu.tw/~d97944007/aggregative/