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Node classification in structural networks is a longstanding important problem in many real-world applications. Recent studies have shown that network embedding can greatly facilitate node classification by employing embedding algorithms to learn feature representations of nodes. Despite of promising performance, existing network embedding based methods are hard to capture the actual category features of a node because of the linearly inseparable problem in low-dimensional space; meanwhile they cannot incorporate both network structure information and node labels information into the representations simultaneously. To address the above problems, this paper presents a novel Deep Kernel Supervised Hashing (DKSH) method to learn hashing representations of nodes for node classification. Specifically, a deep multiple kernel learning is first employed to map nodes into suitable Hilbert space to deal with linearly inseparable problem. Then, instead of only considering structural similarity between two nodes, a novel similarity matrix is designed to merge both network structure information and node labels information. Supervised by the similarity matrix, the learned hashing representations can preserve the two kinds of information simultaneously from the learned Hilbert space. Extensive experiments show that the proposed method significantly outperforms the state-of-the-art baselines over three real-world benchmark datasets. [ABSTRACT FROM AUTHOR]