Recent works demonstrate that GNN models are vulnerable to adversarial
attacks, which refer to imperceptible perturbation on the graph structure and
node features. Among various GNN models, graph contrastive learning (GCL) based
methods specifically suffer from adversarial attacks due to their inherent
design that highly depends on the self-supervision signals derived from the
original graph, which however already contains noise when the graph is
attacked. To achieve adversarial robustness against such attacks, existing
methods adopt adversarial training (AT) to the GCL framework, which considers
the attacked graph as an augmentation under the GCL framework. However, we find
that existing adversarially trained GCL methods achieve robustness at the
expense of not being able to preserve the node feature similarity. In this
paper, we propose a similarity-preserving adversarial graph contrastive
learning (SP-AGCL) framework that contrasts the clean graph with two auxiliary
views of different properties (i.e., the node similarity-preserving view and
the adversarial view). Extensive experiments demonstrate that SP-AGCL achieves
a competitive performance on several downstream tasks, and shows its
effectiveness in various scenarios, e.g., a network with adversarial attacks,
noisy labels, and heterophilous neighbors. Our code is available at
this https URL

本文提出了一种相似性保持的对抗性图对比学习框架 (SP-AGCL)，以对抗攻击进行鲁棒性研究，对比干净的图与两个性质不同的辅助视图 (即节点相似性保持视图和对抗视图)，并通过实验证实了 SP-AGCL 在多个下游任务中的竞争性能和在多种场景下的有效性。

相似性保持的对抗图对比学习

Similarity Preserving Adversarial Graph Contrastive Learning

Locality-sensitive hashing (LSH) is a popular data-independent indexing
method for approximate similarity search, where random projections followed by
quantization hash the points from the database so as to ensure that the
probability of collision is much higher for objects that are close to each
other than for those that are far apart. Most of high-dimensional visual
descriptors for images exhibit a natural matrix structure. When visual
descriptors are represented by high-dimensional feature vectors and long binary
codes are assigned, a random projection matrix requires expensive complexities
in both space and time. In this paper we analyze a bilinear random projection
method where feature matrices are transformed to binary codes by two smaller
random projection matrices. We base our theoretical analysis on extending
Raginsky and Lazebnik's result where random Fourier features are composed with
random binary quantizers to form locality sensitive binary codes. To this end,
we answer the following two questions: (1) whether a bilinear random projection
also yields similarity-preserving binary codes; (2) whether a bilinear random
projection yields performance gain or loss, compared to a large linear
projection. Regarding the first question, we present upper and lower bounds on
the expected Hamming distance between binary codes produced by bilinear random
projections. In regards to the second question, we analyze the upper and lower
bounds on covariance between two bits of binary codes, showing that the
correlation between two bits is small. Numerical experiments on MNIST and
Flickr45K datasets confirm the validity of our method.

本文介绍了一种基于二次随机投影的双线性随机投影方法，通过对特征矩阵进行投影并生成二进制编码，从而实现相似性搜索。实验结果表明该方法具有较高的有效性。