  Graph translation is very promising research direction and has a wide 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 relationships 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.
