NetKit-SRL: A Toolkit for Network Learning and Inference
and its use for classification of networked data
[postscript] [pdf]
Appears in Proceedings of the North American Association for Computational Social and Organizational Science (NAACSOS), June 2005.

Sofus A. Macskassy, Foster Provost.

Abstract

This paper describes NetKit-SRL, or NetKit for short, a toolkit for learning from and classifying networked data. The toolkit is open-source and publicly available. It is modular and built for ease of plug-and-play--such that it is easy to add new modules and have them interact with other existing modules. Currently available NetKit modules are focused on ``batch'' within-network learning and classification: given a partially labeled network, where all nodes and edges are already known to exist, estimate the class membership probability of the unlabeled nodes in the network. NetKit has been used in various network domains such as websites, citation graphs, movies and social networks.