  Two approaches for graph based semi-supervised learning are proposed. The
firstapproach is based on iteration of an affine map. A key element of the
affine map iteration is sparsematrix-vector multiplication, which has several
very efficient parallel implementations. The secondapproach belongs to the
class of Markov Chain Monte Carlo (MCMC) algorithms. It is based onsampling of
nodes by performing a random walk on the graph. The latter approach is
distributedby its nature and can be easily implemented on several processors or
over the network. Boththeoretical and practical evaluations are provided. It is
found that the nodes are classified intotheir class with very small error. The
sampling algorithm's ability to track new incoming nodesand to classify them is
also demonstrated.
