Label Propagation

In metasnf: Meta Clustering with Similarity Network Fusion

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The label propagation procedure can be used to predict cluster membership for new, unlabeled observations based on their similarity to previously labeled observations. These unlabeled observations could be a held out test set from your original sample or a new sample entirely.

The process involves the following steps:

1. Assign clusters to some group of observations
2. Calculate all the pairwise similarities amongst all the already clustered and to-be-labeled observations
3. Run the label propagation algorithm to predict cluster membership in the to-be-labeled observations

There is a lot of room for flexibility in how steps 1 and 2 are conducted. SNF is not necessary at any part of the process. For example, step one could be done by assigning clusters in your training set manually or by a simple clustering method like k-means. Step two could be done just by calculating the euclidean distances across all the training and testing observations for a small subset of features. The features used to calculate the similarities in step 2 don't necessarily need to be the same ones used to derive the cluster solution in the training set either.

A worked example of the label propagation process can be found at the end of the complete example vignette.

metasnf documentation built on June 8, 2025, 12:47 p.m.