  A new algorithm based on bayesian inference for learning local graph
conductance based on Gaussian Process(GP) is given that uses advanced MCMC
convergence ideas to create a scalable and fast algorithm for convergence to
stationary distribution which is provided to learn the bahavior of conductance
when traversing the indirected weighted graph. First metric embedding is used
to represent the vertices of the graph. Then, uniform induced conductance is
calculated for training points. Finally, in the learning step, a gaussian
process is used to approximate the uniform induced conductance. MCMC is used to
measure uncertainty of estimated hyper-parameters.
