Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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Introduction to Modern Information Retrieval
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IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised learning using randomized mincuts
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Classification in Networked Data: A Toolkit and a Univariate Case Study
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DS '09 Proceedings of the 12th International Conference on Discovery Science
Cautious Collective Classification
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Multi-network fusion for collective inference
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Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Network regression with predictive clustering trees
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Batch Mode Active Learning for Networked Data
ACM Transactions on Intelligent Systems and Technology (TIST)
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BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
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This paper is about using multiple types of information for classification of networked data in a semi-supervised setting: given a fully described network (nodes and edges) with known labels for some of the nodes, predict the labels of the remaining nodes. One method recently developed for doing such inference is a guilt-by-association model. This method has been independently developed in two different settings-relational learning and semi-supervised learning. In relational learning, the setting assumes that the networked data has explicit links such as hyperlinks between webpages or citations between research papers. The semi-supervised setting assumes a corpus of non-relational data and creates links based on similarity measures between the instances. Both use only the known labels in the network to predict the remaining labels but use very different information sources. The thesis of this paper is that if we combine these two types of links, the resulting network will carry more information than either type of link by itself. We test this thesis on six benchmark data sets, using a within-network learning algorithm, where we show that we gain significant improvements in predictive performance by combining the links. We describe a principled way of combining mUltiple types of edges with different edge-weights and semantics using an objective graph measure called node-based assortativity. We investigate the use of this measure to combine text-mined links with explicit links and show that using our approach significantly improves performance of our classifier over naively combining these two types of links.