Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
CiteSeer: an automatic citation indexing system
Proceedings of the third ACM conference on Digital libraries
An Introduction to Variational Methods for Graphical Models
Machine Learning
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature value acquisition in testing: a sequential batch test algorithm
ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce
Proceedings of the ninth international conference on Electronic commerce
Exploiting Network Structure for Active Inference in Collective Classification
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Cautious inference in collective classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
VOILA: efficient feature-value acquisition for classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Active learning with statistical models
Journal of Artificial Intelligence Research
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying graphs from noisy and incomplete data
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Reflect and correct: A misclassification prediction approach to active inference
ACM Transactions on Knowledge Discovery from Data (TKDD)
Cautious Collective Classification
The Journal of Machine Learning Research
Identifying graphs from noisy and incomplete data
ACM SIGKDD Explorations Newsletter
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
Collective classification of congressional floor-debate transcripts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Online active inference and learning
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Batch Mode Active Learning for Networked Data
ACM Transactions on Intelligent Systems and Technology (TIST)
On supervised mining of dynamic content-based networks1
Statistical Analysis and Data Mining
Selective sampling on graphs for classification
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning and inference method for within network classification
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Active exploration: simultaneous sampling and labeling for large graphs
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A few good predictions: selective node labeling in a social network
Proceedings of the 7th ACM international conference on Web search and data mining
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Information diffusion, viral marketing, and collective classification all attempt to model and exploit the relationships in a network to make inferences about the labels of nodes. A variety of techniques have been introduced and methods that combine attribute information and neighboring label information have been shown to be effective for collective labeling of the nodes in a network. However, in part because of the correlation between node labels that the techniques exploit, it is easy to find cases in which, once a misclassification is made, incorrect information propagates throughout the network. This problem can be mitigated if the system is allowed to judiciously acquire the labels for a small number of nodes. Unfortunately, under relatively general assumptions, determining the optimal set of labels to acquire is intractable. Here we propose an acquisition method that learns the cases when a given collective classification algorithm makes mistakes, and suggests acquisitions to correct those mistakes. We empirically show on both real and synthetic datasets that this method significantly outperforms a greedy approximate inference approach, a viral marketing approach, and approaches based on network structural measures such as node degree and network clustering. In addition to significantly improving accuracy with just a small amount of labeled data, our method is tractable on large networks.