From bias to opinion: a transfer-learning approach to real-time sentiment analysis
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Making time-stepped applications tick in the cloud
Proceedings of the 2nd ACM Symposium on Cloud Computing
Hypergraph learning with hyperedge expansion
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Collective inference for network data with copula latent markov networks
Proceedings of the sixth ACM international conference on Web search and data mining
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
On the utility of abstraction in labeling actors in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Provost (2007) proposed the weighted-vote relational neighbor classifier (wvRN) as a simple yet effective baseline for semi-supervised learning on network data. It is similar to many recent graph-based SSL methods and is shown to be essentially the same as the Gaussian-field harmonic functions classifier proposed by Zhu et al. (2003) and proves to be very effective on some benchmark network datasets. We describe another simple and intuitive semi-supervised learning method based on random graph walk that outperforms wvRN by a large margin on several benchmark datasets when very few labels are available. Additionally, we show that using authoritative instances as training seeds --- instances that arguably cost much less to label --- dramatically reduces the amount of labeled data required to achieve the same classification accuracy. For some existing state-of-the-art semi-supervised learning methods the labeled data needed is reduced by a factor of 50.