Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active Hidden Markov Models for Information Extraction
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale text categorization by batch mode active learning
Proceedings of the 15th international conference on World Wide Web
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Theoretical Computer Science
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Active learning with statistical models
Journal of Artificial Intelligence Research
Extracting influential nodes on a social network for information diffusion
Data Mining and Knowledge Discovery
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
On active learning of record matching packages
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to infer social ties in large networks
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Batch Mode Active Learning for Networked Data
ACM Transactions on Intelligent Systems and Technology (TIST)
On the non-progressive spread of influence through social networks
LATIN'12 Proceedings of the 10th Latin American international conference on Theoretical Informatics
Actively learning to infer social ties
Data Mining and Knowledge Discovery
Confluence: conformity influence in large social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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We study the problem of active learning for networked data, where samples are connected with links and their labels are correlated with each other. We particularly focus on the setting of using the probabilistic graphical model to model the networked data, due to its effectiveness in capturing the dependency between labels of linked samples. We propose a novel idea of connecting the graphical model to the information diffusion process, and precisely define the active learning problem based on the non-progressive diffusion model. We show the NP-hardness of the problem and propose a method called MaxCo to solve it. We derive the lower bound for the optimal solution for the active learning setting, and develop an iterative greedy algorithm with provable approximation guarantees. We also theoretically prove the convergence and correctness of MaxCo. We evaluate MaxCo on four different genres of datasets: Coauthor, Slashdot, Mobile, and Enron. Our experiments show a consistent improvement over other competing approaches.