  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.
