A Consistent Diffusion-Based Algorithm for Semi-Supervised Classification on Graphs
This addresses a theoretical flaw in a popular graph algorithm, offering a simple fix for improved classification in network analysis.
The paper tackled the inconsistency of heat diffusion for semi-supervised graph classification by proving it fails without centering node temperatures, and showed that this modification yields significant performance gains on real data.
Semi-supervised classification on graphs aims at assigning labels to all nodes of a graph based on the labels known for a few nodes, called the seeds. The most popular algorithm relies on the principle of heat diffusion, where the labels of the seeds are spread by thermo-conductance and the temperature of each node is used as a score function for each label. Using a simple block model, we prove that this algorithm is not consistent unless the temperatures of the nodes are centered before classification. We show that this simple modification of the algorithm is enough to get significant performance gains on real data.