  The quality of a graph is determined jointly by three key factors of the
graph: nodes, edges and similarity measure (or edge weights), and is very
crucial to the success of graph-based semi-supervised learning (SSL)
approaches. Recently, dynamic graph, which means part/all its factors are
dynamically updated during the training process, has demonstrated to be
promising for graph-based semi-supervised learning. However, existing
approaches only update part of the three factors and keep the rest manually
specified during the learning stage. In this paper, we propose a novel
graph-based semi-supervised learning approach to optimize all three factors
simultaneously in an end-to-end learning fashion. To this end, we concatenate
two neural networks (feature network and similarity network) together to learn
the categorical label and semantic similarity, respectively, and train the
networks to minimize a unified SSL objective function. We also introduce an
extended graph Laplacian regularization term to increase training efficiency.
Extensive experiments on several benchmark datasets demonstrate the
effectiveness of our approach.
