Solving Electrical Networks to incorporate Supervision in Random Walks

Random walks is one of the most popular ideas in computer science. A critical assumption in random walks is that the probability of the walk being at a given vertex at a time instance converges to a limit independent of the start state. While this makes it computationally efficient to solve, it limits their use to incorporate label information. In this paper, we exploit the connection between Random Walks and Electrical Networks to incorporate label information in classification, ranking, and seed expansion