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Graph-structured data naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, graph mining is a challenging task due to the underlying complex and diverse connectivity patterns. A potential solution is to learn the representation of a graph in a low-dimensional Euclidean space via embedding techniques that preserve the graph properties. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. On the other hand, deep learning models on graphs have recently emerged in both machine learning and data mining areas and demonstrated superior performance for …