Graph Label Propagation

Graph label propagation is a semi-supervised machine learning algorithm that infers labels for unlabelled nodes in a graph based on the labels of a few known nodes and the weighted edges connecting them. This process can be interpreted as finding the harmonic function on the graph, which minimizes the total energy (or tension) of the edges.

Experiment

Use the module controls to explore how edge weights influence the label propagation:

1. Set Left edge weight, Middle edge weight and Right edge weight to 1. Read node scores 0.333333 and 0.666667, and energy 0.333333.
2. Set Left edge weight to 2, keeping the other two at 1. Read scores 0.200000 and 0.600000, and energy 0.400000. The left edge more strongly pulls its adjacent score toward zero.
3. Set all three edge weights to 4. The scores return to 0.333333 and 0.666667, while energy rises to 1.333333: uniform weight scaling changes energy, not the minimizer.
4. Drag the graph or use arrow keys to pan. Press Home or Reset view to return without changing the weights or scores.