Unsupervised Adversarially-Robust Representation Learning on Graphs
- URL: http://arxiv.org/abs/2012.02486v1
- Date: Fri, 4 Dec 2020 09:29:16 GMT
- Title: Unsupervised Adversarially-Robust Representation Learning on Graphs
- Authors: Jiarong Xu, Junru Chen, Yang Yang, Yizhou Sun, Chunping Wang, Jiangang
Lu
- Abstract summary: Recent works have demonstrated that deep learning on graphs is vulnerable to adversarial attacks.
In this paper, we focus on the underlying problem of learning robust representations on graphs via mutual information.
- Score: 26.48111798048012
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Recent works have demonstrated that deep learning on graphs is vulnerable to
adversarial attacks, in that imperceptible perturbations on input data can lead
to dramatic performance deterioration. In this paper, we focus on the
underlying problem of learning robust representations on graphs via mutual
information. In contrast to previous works measure the task-specific robustness
based on the label space, we here take advantage of the representation space to
study a task-free robustness measure given the joint input space w.r.t graph
topology and node attributes. We formulate this problem as a constrained saddle
point optimization problem and solve it efficiently in a reduced search space.
Furthermore, we provably establish theoretical connections between our
task-free robustness measure and the robustness of downstream classifiers.
Extensive experiments demonstrate that our proposed method is able to enhance
robustness against adversarial attacks on graphs, yet even increases natural
accuracy.
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