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Collective Classification in Network Data
TLDR
This article introduces four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data. Expand
Learning Probabilistic Relational Models
TLDR
This paper describes both parameter estimation and structure learning -- the automatic induction of the dependency structure in a model and shows how the learning procedure can exploit standard database retrieval techniques for efficient learning from large datasets. Expand
Introduction to statistical relational learning
TLDR
In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. Expand
Collective entity resolution in relational data
TLDR
This work proposes a novel relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities, and gives an efficient implementation and investigates the impact that different relational similarity measures have on entity resolution quality. Expand
Link-based Classification
Link mining: a survey
TLDR
While network analysis has been studied in depth in particular areas such as social network analysis, hypertext mining, and web analysis, only recently has there been a cross-fertilization of ideas among these different communities. Expand
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
TLDR
This book is intended to be a guide to the art of self-consistency and should not be relied on as a substitute for professional advice on how to deal with ambiguity. Expand
Query-driven Active Surveying for Collective Classification
TLDR
This work develops an algorithm which adaptively selects survey nodes by estimating which form of smoothness is most appropriate, and evaluates its algorithm on several network datasets and demonstrates its improvements over standard active learning methods. Expand
A short introduction to probabilistic soft logic
TLDR
This paper provides an overview of the PSL language and its techniques for inference and weight learning. Expand
To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles
TLDR
This work shows how an adversary can exploit an online social network with a mixture of public and private user profiles to predict the private attributes of users, and proposes practical models that use friendship and group membership information to infer sensitive attributes. Expand
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