This paper focuses on learning representation on the whole graph level in an
unsupervised manner. Learning graph-level representation plays an important
role in a variety of real-world issues such as molecule property prediction,
protein structure feature extraction, and social network analysis. The
mainstream method is utilizing contrastive learning to facilitate graph feature
extraction, known as Graph Contrastive Learning (GCL). GCL, although effective,
suffers from some complications in contrastive learning, such as the effect of
false negative pairs. Moreover, augmentation strategies in GCL are weakly
adaptive to diverse graph datasets. Motivated by these problems, we propose a
novel framework called Structure Knowledge Refinement (SKR) which uses data
structure to determine the probability of whether a pair is positive or
negative. Meanwhile, we propose an augmentation strategy that naturally
preserves the semantic meaning of the original data and is compatible with our
SKR framework. Furthermore, we illustrate the effectiveness of our SKR
framework through intuition and experiments. The experimental results on the
tasks of graph-level classification demonstrate that our SKR framework is
superior to most state-of-the-art baselines.

本文提出了一种名为 “结构知识细化” 的新框架，用于在无监督的情况下学习整个图形级别的表示，该框架通过数据结构确定一对数据是否为正或负，并通过直观观察和实验证明了其在图形级别分类任务上优于大多数最先进的对照方法。

基于结构知识精细化的图级蛋白质表示学习

Graph-level Protein Representation Learning by Structure Knowledge  Refinement

Graph Neural Networks (GNNs) are widely adopted to analyse non-Euclidean
data, such as chemical networks, brain networks, and social networks, modelling
complex relationships and interdependency between objects. Recently, Membership
Inference Attack (MIA) against GNNs raises severe privacy concerns, where
training data can be leaked from trained GNN models. However, prior studies
focus on inferring the membership of only the components in a graph, e.g., an
individual node or edge. How to infer the membership of an entire graph record
is yet to be explored.
In this paper, we take the first step in MIA against GNNs for graph-level
classification. Our objective is to infer whether a graph sample has been used
for training a GNN model. We present and implement two types of attacks, i.e.,
training-based attacks and threshold-based attacks from different adversarial
capabilities. We perform comprehensive experiments to evaluate our attacks in
seven real-world datasets using five representative GNN models. Both our
attacks are shown effective and can achieve high performance, i.e., reaching
over 0.7 attack F1 scores in most cases. Furthermore, we analyse the
implications behind the MIA against GNNs. Our findings confirm that GNNs can be
even more vulnerable to MIA than the models with non-graph structures. And
unlike the node-level classifier, MIAs on graph-level classification tasks are
more co-related with the overfitting level of GNNs rather than the statistic
property of their training graphs.

本论文针对图神经网络的成员推断攻击扩展到了整个图记录的成员推断，通过不同的攻击能力实现了两种攻击类型：基于训练的攻击和基于阈值的攻击，并在七个真实数据集中使用五种代表性的 GNN 模型进行了全面的实验评估，结果表明我们的攻击方法是有效的，对于图级分类任务的 MIAs 更多地与 GNN 的过拟合水平有关。

将成员推理攻击应用于 GNN 图分类：方法和影响

Adapting Membership Inference Attacks to GNN for Graph Classification:  Approaches and Implications

Predicating macroscopic influences of drugs on human body, like efficacy and
toxicity, is a central problem of small-molecule based drug discovery.
Molecules can be represented as an undirected graph, and we can utilize graph
convolution networks to predication molecular properties. However, graph
convolutional networks and other graph neural networks all focus on learning
node-level representation rather than graph-level representation. Previous
works simply sum all feature vectors for all nodes in the graph to obtain the
graph feature vector for drug predication. In this paper, we introduce a dummy
super node that is connected with all nodes in the graph by a directed edge as
the representation of the graph and modify the graph operation to help the
dummy super node learn graph-level feature. Thus, we can handle graph-level
classification and regression in the same way as node-level classification and
regression. In addition, we apply focal loss to address class imbalance in drug
datasets. The experiments on MoleculeNet show that our method can effectively
improve the performance of molecular properties predication.

本文提出了一种新的方法，使用具有所有节点连接的虚拟超级节点来表示整张图并改进图像运算以帮助超级节点学习整体特征，同时采用集中损失来处理药物数据集中的类别不平衡问题。该方法显著提高了小分子属性预测的性能。