This paper studies four Graph Neural Network architectures (GNNs) for a graph
classification task on a synthetic dataset created using classic generative
models of Network Science. Since the synthetic networks do not contain (node or
edge) features, five different augmentation strategies (artificial feature
types) are applied to nodes. All combinations of the 4 GNNs (GCN with
Hierarchical and Global aggregation, GIN and GATv2) and the 5 feature types
(constant 1, noise, degree, normalized degree and ID -- a vector of the number
of cycles of various lengths) are studied and their performances compared as a
function of the hidden dimension of artificial neural networks used in the
GNNs. The generalisation ability of these models is also analysed using a
second synthetic network dataset (containing networks of different sizes).Our
results point towards the balanced importance of the computational power of the
GNN architecture and the the information level provided by the artificial
features. GNN architectures with higher computational power, like GIN and
GATv2, perform well for most augmentation strategies. On the other hand,
artificial features with higher information content, like ID or degree, not
only consistently outperform other augmentation strategies, but can also help
GNN architectures with lower computational power to achieve good performance.

本研究对四种图神经网络架构及五种节点人工特征进行分析，应用于分类任务，并比较它们在不同隐藏层维度下的性能。结果表明，高计算能力的 GNN 架构与信息丰富的人工特征对于性能表现起到平衡重要作用。

关于图神经网络和特征增强策略在社交网络分类中的能力

On the Power of Graph Neural Networks and Feature Augmentation  Strategies to Classify Social Networks

Simultaneous machine translation (SiMT) outputs the translation while
receiving the source inputs, and hence needs to balance the received source
information and translated target information to make a reasonable decision
between waiting for inputs or outputting translation. Previous methods always
balance source and target information at the token level, either directly
waiting for a fixed number of tokens or adjusting the waiting based on the
current token. In this paper, we propose a Wait-info Policy to balance source
and target at the information level. We first quantify the amount of
information contained in each token, named info. Then during simultaneous
translation, the decision of waiting or outputting is made based on the
comparison results between the total info of previous target outputs and
received source inputs. Experiments show that our method outperforms strong
baselines under and achieves better balance via the proposed info.

本文提出了一种基于信息量的等待策略来平衡源语言信息和目标语言信息的机器翻译方法，用于在同时接收源输入和输出翻译结果时做出合理决策。实验结果表明，该方法在实现平衡方面表现出色，比其他基线方法效果更好。