NetKit is an open-source Network Learning toolkit for statistical relational learning. Its architecture is extremely modular, making it easy to combine different learning algorithms.
It is written in Java 1.5 and its plug-and-play architecture makes it possible to mix-and-match different components in the relational learning process: the non-relational classifier, the relational classifier and the collective inference algorithm. It integrates seamlessly with the Weka machine learning toolkit, making it possible to use any of Weka's learning classifiers in the context of relational learning.
The NetKit architecture is designed primarily to support statistical relational learning and inference on relational data. It represents relational data as a graph over which it does collective inference to make various predictions of attributes.
NetKit is also built to efficiently represent more complex relational data, such as multiple entities in directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for quickly compute various graph statistics on large graphs. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities. The current distribution of NetKit includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as statistical analysis, and calculation of network distances, flows, and importance measures (various centrality metrics).
- Changes to previous version:
Initial Announcement on mloss.org.
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