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Collective Classification in Network Data
- P. Sen, Galileo Namata, M. Bilgic, L. Getoor, B. Gallagher, Tina Eliassi-Rad
- Computer ScienceAI Mag.
- 6 September 2008
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.
A key challenge for machine learning is tackling the problem of mining richly structured data sets, where the objects are linked in some way due to either an explicit or implicit relationship that…
Learning Probabilistic Relational Models
- L. Getoor
- Computer ScienceIJCAI
- 31 July 1999
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.
Collective entity resolution in relational data
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.
Query-driven Active Surveying for Collective Classification
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.
Introduction to statistical relational learning
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.
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
Link mining: a survey
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.
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
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.
To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles
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.