Graph neural networks have become the standard approach for dealing with
learning problems on graphs. Among the different variants of graph neural
networks, graph attention networks (GATs) have been applied with great success
to different tasks. In the GAT model, each node assigns an importance score to
its neighbors using an attention mechanism. However, similar to other graph
neural networks, GATs aggregate messages from nodes that belong to different
classes, and therefore produce node representations that are not well separated
with respect to the different classes, which might hurt their performance. In
this work, to alleviate this problem, we propose a new technique that can be
incorporated into any graph attention model to encourage higher attention
scores between nodes that share the same class label. We evaluate the proposed
method on several node classification datasets demonstrating increased
performance over standard baseline models.

本文提出了一种改进图注意力网络中节点分类的方法，使其更能准确地区分不同类别的节点，在多个节点分类数据集上得到了较高的表现。

使用同构性在图神经网络中进行监督关注

Supervised Attention Using Homophily in Graph Neural Networks

Class labels have been empirically shown useful in improving the sample
quality of generative adversarial nets (GANs). In this paper, we mathematically
study the properties of the current variants of GANs that make use of class
label information. With class aware gradient and cross-entropy decomposition,
we reveal how class labels and associated losses influence GAN's training.
Based on that, we propose Activation Maximization Generative Adversarial
Networks (AM-GAN) as an advanced solution. Comprehensive experiments have been
conducted to validate our analysis and evaluate the effectiveness of our
solution, where AM-GAN outperforms other strong baselines and achieves
state-of-the-art Inception Score (8.91) on CIFAR-10. In addition, we
demonstrate that, with the Inception ImageNet classifier, Inception Score
mainly tracks the diversity of the generator, and there is, however, no
reliable evidence that it can reflect the true sample quality. We thus propose
a new metric, called AM Score, to provide a more accurate estimation of the
sample quality. Our proposed model also outperforms the baseline methods in the
new metric.

本文系统研究了当前使用类别标签的生成对抗网络 GANs 以及相关的分类信息对 GAN 网络的训练和生成效果的影响，并提出了一种新的基于激活最大化的 AM-GAN 生成对抗网络方案，在 CIFAR-10 数据集上实现了 8.91 的 state-of-the-art Inception 得分，并提出 AM 得分评价指标以更准确地评估生成样本质量。