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Collective Classification in Network Data
Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links),Expand
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Learning Probabilistic Relational Models
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat" data representations. Thus, to apply theseExpand
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Introduction to statistical relational learning
Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas fromExpand
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Collective entity resolution in relational data
Many databases contain uncertain and imprecise references to real-world entities. The absence of identifiers for the underlying entities often results in a database which contains multiple referencesExpand
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Link-based Classification
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Link mining: a survey
Many datasets of interest today are best described as a linked collection of interrelated objects. These may represent homogeneous networks, in which there is a single-object type and link type, orExpand
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Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) withoutExpand
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To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles
In order to address privacy concerns, many social media websites allow users to hide their personal profiles from the public. In this work, we show how an adversary can exploit an online socialExpand
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A short introduction to probabilistic soft logic
Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over randomExpand
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An Introduction to Conditional Random Fields for Relational Learning
This chapter contains sections titled: Introduction, Graphical Models, Linear-Chain Conditional Random Fields, CRFs in General, Skip-Chain CRFs, Conclusion, Acknowledgments, References
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