Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Exploiting relational structure to understand publication patterns in high-energy physics
ACM SIGKDD Explorations Newsletter
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Using relational knowledge discovery to prevent securities fraud
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A statistical framework for genomic data fusion
Bioinformatics
Web page classification with heterogeneous data fusion
Proceedings of the 16th international conference on World Wide Web
Relational Dependency Networks
The Journal of Machine Learning Research
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Genetic fuzzy classification fusion of multiple SVMs for biomedical data
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary computation in bioinformatics
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A bias/variance decomposition for models using collective inference
Machine Learning
Improving learning in networked data by combining explicit and mined links
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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Although much of the recent work in statistical relational learning has focused on homogeneous networks, many relational domains naturally consist of multiple observed networks, where each network source records a different type of relationship between the same set of entities. For example, data about organizations may contain both an email communication network and a network of coworker ties. Since collective classification models rely on propagating information throughout the relational network to improve predictions, multi-network methods will need to consider how to best combine relational information from various link sources. There are two opportunities to combine multi-source link information for relational classification: data fusion methods combine the available information during learning, while classification fusion methods learn and apply models independently on each network source, then combine model predictions during inference. Past work has focused primarily on data fusion techniques, where features and/or links from various sources are combined. However, as the number of links, sources, and/or features increases, this approach can lead to high variance in the learned model, and can also increase the amount of noise propagated during inference, which will degrade performance. In this work, we focus on classification fusion, which overcomes these limitations by learning independent models to reduce variance. In addition, we develop a novel approach to collective fusion, which interleaves the learned models during collective inference. We evaluate our methods on synthetic and real-world social network data, showing that collective fusion significantly outperforms other methods over a wide range of conditions.