Abstract Link prediction and network alignment are two important problems in social network analysis and other network related applications. Considerable efforts have been devoted to these two problems while often in an independent way to each other. In this paper, we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation. Our approach can either work with a few initial vertex correspondences as seeds or from scratch. By extensive experiments on public benchmarks, we show that link prediction and network alignment can benefit each other especially for improving the recall for both tasks