  The goal in semi-supervised learning is to effectively combine labeled and
unlabeled data. One way to do this is by encouraging smoothness across edges in
a graph whose nodes correspond to input examples. In many graph-based methods,
labels can be thought of as propagating over the graph, where the underlying
propagation mechanism is based on random walks or on averaging dynamics. While
theoretically elegant, these dynamics suffer from several drawbacks which can
hurt predictive performance.
  Our goal in this work is to explore alternative mechanisms for propagating
labels. In particular, we propose a method based on dynamic infection
processes, where unlabeled nodes can be "infected" with the label of their
already infected neighbors. Our algorithm is efficient and scalable, and an
analysis of the underlying optimization objective reveals a surprising relation
to other Laplacian approaches. We conclude with a thorough set of experiments
across multiple benchmarks and various learning settings.
