首页|Identity-Preserving Adversarial Training for Robust Network Embedding
Identity-Preserving Adversarial Training for Robust Network Embedding
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Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link prediction.Unfortunately,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream tasks.To achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ex-amples.However,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial training.In this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving regularization.We formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original node.Extensive experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.
network embeddingidentity-preservingadversarial trainingadversarial the example
岑科廷、沈华伟、曹婍、徐冰冰、程学旗
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Data Intelligence System Research Center,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
University of Chinese Academy of Sciences,Beijing 101480,China
Beijing Academy of Artificial Intelligence,Beijing 100000,China
Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology,Institute of Computing Technology Chinese Academy of Sciences,Beijing 100190,China
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国家自然科学基金国家自然科学基金国家重点研发计划CCF-Tencent Open Research FundBeijing Academy of Artificial Intelligence(BAAI)
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