Deep Multiattributed Graph Translation with NodeEdge CoEvolution 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 fastincreasing 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, endtoend framework for joint node and edge attributes prediction, which is a need for realworld applications such as malware confinement in IoT networks and structuraltofunctional network translation. These realworld applications highly depend on handcrafting and adhoc heuristic models, but cannot sufficiently utilize massive historical data. In this paper, we termed this generic problem multiattributed 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 nonparametric graph Laplacian is proposed to learn and maintain the consistency of the predicted nodes and edges. Finally, extensive experiments on both synthetic and realworld application data demonstrated the effectiveness of the proposed method.

Collaborative Graph Walk for SemiSupervised Multilabel Node Classification In this work, we study semisupervised multilabel node classification problem in attributed graphs. Classic solutions to multilabel node classification follow two steps, first learn node embedding and then build a node classifier on the learned embedding. To improve the discriminating power of the node embedding, we propose a novel collaborative graph walk, named MultiLabelGraphWalk, to finely tune node representations with the available label assignments in attributed graphs via reinforcement learning. The proposed method formulates the multilabel node classification task as simultaneous graph walks conducted by multiple labelspecific agents. Furthermore, policies of the labelwise graph walks are learned in a cooperative way to capture first the predictive relation between node labels and structural attributes of graphs; and second, the correlation among the multiple labelspecific classification tasks. A comprehensive experimental study demonstrates that the proposed method can achieve significantly better multilabel classification performance than the stateoftheart approaches and conduct more efficient graph exploration.