Learning with latent networks
Overview
This is a new framework in which graph-structured data are used to train a classifier in a latent space, and then classify new nodes. During the learning phase, a latent representation of the network is first learned and a supervised classifier is then built in the learned latent space. In order to classify new nodes, the positions of these nodes in the learned latent space are estimated using the existing links between the new nodes and the learning set nodes. It is then possible to apply the supervised classifier to assign each new node to one of the classes.
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Documentation
- Help pages
- 5includes a package overview page
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 67%
- Help pages citing references
- 80%
Also includes 1 demo script (run with demo())
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People & History
Package metadata
- First published
- 2007-07-04
- Total releases
- 3 / 19 yrs
- License
- GPL2.0
- Bundled data
- 0.3 KB / 1 file
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