Learning to extract symbolic knowledge from the World Wide Web
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Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
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Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting
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Computationally efficient scoring of activity using demographics and connectivity of entities
Information Technology and Management
Label-dependent feature extraction in social networks for node classification
SocInfo'10 Proceedings of the Second international conference on Social informatics
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ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Leveraging Network Properties for Trust Evaluation in Multi-agent Systems
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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This paper surveys work from the field of machine learning on the problem of within-network learning and inference. To give motivation and context to the rest of the survey, we start by presenting some (published) applications of within-network inference. After a brief formulation of this problem and a discussion of probabilistic inference in arbitrary networks, we survey machine learning work applied to networked data, along with some important predecessors--mostly from the statistics and pattern recognition literature. We then describe an application of within-network inference in the domain of suspicion scoring in social networks. We close the paper with pointers to toolkits and benchmark data sets used in machine learning research on classification in network data. We hope that such a survey will be a useful resource to workshop participants, and perhaps will be complemented by others.