Graph translation is very promising research direction and has awide range of potential real-world applications. Graph is a natural structure for representing relationship and interactions, and its translation can encode the intrinsic semantic changes of relation-ships in different scenarios. However, despite its seemingly wide possibilities, usage of graph translation so far is still quite limited.One important reason is the lack of high-quality paired dataset. For example, we can easily build graphs representing peoples? shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain. Therefore,in this work, we seek to provide a graph translation model in the semi-supervised scenario. This task is non-trivial, because graph translation involves changing the semantics in the form of link topology and node attributes, which is difficult to capture due to the combinatory nature and inter-dependencies. Furthermore, due to the high order of freedom in graph's composition, it is difficult to assure the generalization ability of trained models. These difficulties impose a tighter requirement for the exploitation of unpaired samples. Addressing them, we propose to construct a dual representation space, where transformation is performed explicitly to model the semantic transitions. Special encoder/decoder structures are designed, and auxiliary mutual information loss is also adopted to enforce the alignment of unpaired/paired examples. We evaluate the proposed method in three different datasets.