In this paper, we propose an efficient and robust algorithm for graph-based transductive classification. After approximating a graph with a spanning tree, we develop a linear-time algorithm to label the tree such that the cut size of the tree is minimized. This significantly improves typical graph-based methods, which either have a cubic time complexity (for a dense graph) or $O(kn^2)$ (for a sparse graph with denoting the node degree). %In addition to its great scalability on large data, our proposed algorithm demonstrates high robustness and accuracy. In particular, on a graph with 400,000 nodes (in which 10,000 nodes are labeled) and 10,455,545 edges, our algorithm achieves the highest accuracy of but takes less than seconds to label all the unlabeled data. Furthermore, our method shows great robustness to the graph construction both theoretically and empirically, this overcomes another big problem of traditional graph-based methods. In addition to its good scalability and robustness, the proposed algorithm demonstrates high accuracy. In particular, on a graph with nodes (in which nodes are labeled) and edges, our algorithm achieves the highest accuracy of but takes less than seconds to label all the unlabeled data.