Simple Models and Classification in Networked Data

9 Pages Posted: 9 Oct 2008

See all articles by Sofus Macskassy

Sofus Macskassy

Fetch Technologies, Inc

Foster Provost

New York University

Date Written: 2004


When entities are linked by explicit relations, classification methodsthat take advantage of the network can perform substantiallybetter than methods that ignore the network. This paper arguesthat studies of relational classification in networked data shouldinclude simple network-only methods as baselines for comparison,in addition to the non-relational baselines that generally areused. In particular, comparing more complex algorithms with algorithmsthat only consider the network (and not the features ofthe entities) allows one to factor out the contribution of the networkstructure itself to the predictive power of the model. Weexamine several simple methods for network-only classificationon previously used relational data sets, and show that they canperform remarkably well. The results demonstrate that the inclusionof network-only classifiers can shed new light on studies ofrelational learners.

Suggested Citation

Macskassy, Sofus and Provost, Foster, Simple Models and Classification in Networked Data (2004). NYU Working Paper No. 2451/14117, Available at SSRN:

Sofus Macskassy (Contact Author)

Fetch Technologies, Inc ( email )

2041 Rosecrans Ave
Suite 245
El Segundo, CA 90245
United States


Foster Provost

New York University ( email )

44 West Fourth Street
New York, NY 10012
United States

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