Collective classification in network data (2008)

by Prithviraj Sen , Galileo Namata , Mustafa Bilgic , Lise Getoor , Brian Gallagher , Tina Eliassi-rad
Citations:45 - 17 self

Active Bibliography

7 Empirical comparison of approximate inference algorithms for networked data – Prithviraj Sen, Lise Getoor - 2006
Approved as to style and content by: – Charles A. Sutton, Sridhar Mahadevan Member, Jonathan Machta Member, Tommi Jaakkola Member, Andrew Barto, Department Chair, Hanna Wallach, Max Welling - 2008
2 A brief survey of machine learning methods for classification in networked data and an application to suspicion scoring – Sofus A. Macskassy, Foster Provost - 2006
1 Cost-Sensitive Learning with Conditional Markov Networks – Prithviraj Sen, Lise Getoor
1 Classification in networked data – A Toolkit, Sofus A. Macskassy, Foster Provost, Andrew Mccallum - 2006
Classification in networked data -- A Toolkit and a . . . – Sofus A. Macskassy, Foster Provost - 2007
Collective Classification in Network Data Articles – Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, Tina Eliassi-rad
Proposed design for gR, a graphical models toolkit for R – Kevin P. Murphy - 2003
393 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
7 Extending expectation propagation for graphical models – Yuan Qi - 2004
279 Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms – Jonathan S. Yedidia, William T. Freeman, Yair Weiss - 2005
Learning Symmetric Relational Markov Random Fields – Ofer Meshi, Supervised Prof, Nir Friedman - 2007
39 Location-based activity recognition – Lin Liao, Dieter Fox, Henry Kautz - 2005
1 GRAPH BASED IMAGE SEGMENTATION – Jingdong Wang - 2007
19 Towards an integrated protein-protein interaction network – Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman - 2005
6 Join-Graph Propagation Algorithms – Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina Dechter
Extended Version of "Expectation propagation for approximate inference in dynamic Bayesian networks" – Tom Heskes, Onno Zoeter - 2003
18 Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies – Tom Heskes
14 Unsupervised learning – Zoubin Ghahramani - 2004