Practical data analysis and mining rarely falls exactly into the supervised learning scenario. Rather, the growing amount of unlabelled data from various scientific domains poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computational intensiveness of graph-based SSL arises largely from the manifold or graph regularization, which may in turn lead to large models that are difficult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highly scalable, graph-based algorithm for large-scale SSL. Our key innovation is the use of “prototypes vectors” for efficient approximation on both the graph-based regularizer and the model representation. The choice of prototypes are grounded upon two important criterion: they not only perform effective low- rank approximation on the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. These criterion lead to consistent prototype selection scheme, allowing us to design a unified algorithm (PVM) that demonstrates encouraging performance while at the same time possessing appealing scaling properties (empirically linear with sample size).