Graph embedding is the process of obtaining a vector representation of graph nodes. The representation obtained by graph embedding is highly versatile. It can be used for various tasks, such as recommendation and clustering tasks. However, there are only a few methods that incorporate attributes indicating node characteristics, such as user gender, age, or product category, into graph embedding. Therefore, we hypothesize that nodes with the same attribute are often connected to the same node. Consequently, we propose two methods for graph embedding, parallel and serial, that use metric learning to reflect attribute data in node features. The proposed method can be applied to any graph embedding and metric learning method, and thus can also be applied to many new methods yet to be developed. Numerical experimental results show that the proposed method using node attributes is superior to the existing methods in both AUROC and accuracy.

Deep learning models are vulnerable to adversarial examples, and adversarial attacks used to generate such examples have attracted considerable research interest. Although existing methods based on the steepest descent have achieved high attack success rates, ill-conditioned problems occasionally reduce their performance. To address this limitation, we utilize the conjugate gradient (CG) method, which is effective for this type of problem, and propose a novel attack algorithm inspired by the CG method, named the Auto Conjugate Gradient (ACG) attack. The results of large-scale evaluation experiments conducted on the latest robust models show that, for most models, ACG was able to find more adversarial examples with fewer iterations than the existing SOTA algorithm Auto-PGD (APGD). We investigated the difference in search performance between ACG and APGD in terms of diversification and intensification, and define a measure called Diversity Index (DI) to quantify the degree of diversity. From the analysis of the diversity using this index, we show that the more diverse search of the proposed method remarkably improves its attack success rate.