Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning - Ding, Daizong; Zhang, Mi; Pan, Xudong; Yang, Min; He, Xiangnan (Proceedings of the AAAI Conference on Artificial Intelligence, 2020)
Node embedding is a crucial task in graph analysis. Recently, several methods are proposed to embed a node as a distribution rather than a vector to capture more information. Although these methods achieved noticeable improvements, their extra complexity brings new challenges. For example, the learned representations of nodes could be sensitive to external noises on the graph and vulnerable to adversarial behaviors. In this paper, we first derive an upper bound on generalization error for Wasserstein embedding via the PAC-Bayesian theory. Based on this, we propose an algorithm called Adversarial PAC-Bayesian Learning (APBL) in order to minimize the generalization error bound. Furthermore, we provide a model called Regularized Adversarial Wasserstein Embedding Network (RAWEN) as an implementation of APBL. Besides our comprehensive analysis of the robustness of RAWEN, our work for the first time explores more kinds of embedded distributions. For evaluations, we conduct extensive experiments to demonstrate the effectiveness and robustness of our proposed embedding model compared with the state-of-the-art methods.