Graph neural networks (GNNs) achieve remarkable success in graph-based
semi-supervised node classification, leveraging the information from
neighboring nodes to improve the representation learning of target node. The
success of GNNs at node classification depends on the assumption that connected
nodes tend to have the same label. However, such an assumption does not always
work, limiting the performance of GNNs at node classification. In this paper,
we propose label-consistency based graph neural network(LC-GNN), leveraging
node pairs unconnected but with the same labels to enlarge the receptive field
of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed
LC-GNN outperforms traditional GNNs in graph-based semi-supervised node
classification.We further show the superiority of LC-GNN in sparse scenarios
with only a handful of labeled nodes.

本文提出了基于标签一致性的图神经网络（LC-GNN），扩大了 GNN 中的节点感受野，利用未连接但标签相同的节点对。在基准数据集上的实验证明，LC-GNN 在图形半监督节点分类方面优于传统的 GNN，在只有少数标记节点的稀疏场景中也表现出优越性。

基于标签一致性的图神经网络用于半监督节点分类

Label-Consistency based Graph Neural Networks for Semi-supervised Node  Classification

Deep neural networks have been demonstrated to be vulnerable to backdoor
attacks. Specifically, by injecting a small number of maliciously constructed
inputs into the training set, an adversary is able to plant a backdoor into the
trained model. This backdoor can then be activated during inference by a
backdoor trigger to fully control the model's behavior. While such attacks are
very effective, they crucially rely on the adversary injecting arbitrary inputs
that are---often blatantly---mislabeled. Such samples would raise suspicion
upon human inspection, potentially revealing the attack. Thus, for backdoor
attacks to remain undetected, it is crucial that they maintain
label-consistency---the condition that injected inputs are consistent with
their labels. In this work, we leverage adversarial perturbations and
generative models to execute efficient, yet label-consistent, backdoor attacks.
Our approach is based on injecting inputs that appear plausible, yet are hard
to classify, hence causing the model to rely on the (easier-to-learn) backdoor
trigger.

本文利用敌对扰动和生成模型执行高效且标签一致的后门攻击，通过注入似乎合理但难以分类的输入来使模型依赖于（易于学习的）后门触发器，达到攻击的目的。