  Generalized from image and language translation, graph translation aims to
generate a graph in the target domain by conditioning an input graph in the
source domain. This promising topic has attracted fast-increasing attention
recently. Existing works are limited to either merely predicting the node
attributes of graphs with fixed topology or predicting only the graph topology
without considering node attributes, but cannot simultaneously predict both of
them, due to substantial challenges: 1) difficulty in characterizing the
interactive, iterative, and asynchronous translation process of both nodes and
edges and 2) difficulty in discovering and maintaining the inherent consistency
between the node and edge in predicted graphs. These challenges prevent a
generic, end-to-end framework for joint node and edge attributes prediction,
which is a need for real-world applications such as malware confinement in IoT
networks and structural-to-functional network translation. These real-world
applications highly depend on hand-crafting and ad-hoc heuristic models, but
cannot sufficiently utilize massive historical data. In this paper, we termed
this generic problem "multi-attributed graph translation" and developed a novel
framework integrating both node and edge translations seamlessly. The novel
edge translation path is generic, which is proven to be a generalization of the
existing topology translation models. Then, a spectral graph regularization
based on our non-parametric graph Laplacian is proposed in order to learn and
maintain the consistency of the predicted nodes and edges. Finally, extensive
experiments on both synthetic and real-world application data demonstrated the
effectiveness of the proposed method.
