RaWaNet：通过图上的随机游动丰富图神经网络输入
RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs
摘要：
近年来，图神经网络（GNN）越来越受欢迎，并且对于由图表示的数据显示出非常有前途的结果。大多数GNN架构都是基于开发新的卷积和/或池化层来设计的，这些层可以更好地提取用于不同预测任务的图的隐藏和更深层次的表示。这些层的输入主要是图的三个默认描述符：节点特征$(X)$、邻接矩阵$(A)$和边特征$(W)$（如果可用）。为了向网络提供更丰富的输入，我们提出了基于三个选定长度的图的随机游动数据处理。即，长度为1和2的（规则）行走，以及长度为$γ\in (0,1)$的分数行走，以捕获图上不同的局部和全局动态。我们还计算每个随机游走的平稳分布，然后将其用作初始节点特征（$X$）的缩放因子。这样，对于每个图，网络接收多个邻接矩阵以及它们对节点特征的单独权重。我们在各种分子数据集上测试我们的方法，将处理后的节点特征传递给网络，以便执行几个分类和回归任务。有趣的是，我们的方法没有使用在分子图学习中被大量利用的边缘特征，让浅层网络的性能优于众所周知的深度GNN。
英文摘要：
In recent years, graph neural networks (GNNs) have gained increasing popularity and have shown very promising results for data that are represented by graphs. The majority of GNN architectures are designed based on developing new convolutional and/or pooling layers that better extract the hidden and deeper representations of the graphs to be used for different prediction tasks. The inputs to these layers are mainly the three default descriptors of a graph, node features $(X)$, adjacency matrix $(A)$, and edge features $(W)$ (if available). To provide a more enriched input to the network, we propose a random walk data processing of the graphs based on three selected lengths. Namely, (regular) walks of length 1 and 2, and a fractional walk of length $γ\in (0,1)$, in order to capture the different local and global dynamics on the graphs. We also calculate the stationary distribution of each random walk, which is then used as a scaling factor for the initial node features ($X$). This way, for each graph, the network receives multiple adjacency matrices along with their individual weighting for the node features. We test our method on various molecular datasets by passing the processed node features to the network in order to perform several classification and regression tasks. Interestingly, our method, not using edge features which are heavily exploited in molecular graph learning, let a shallow network outperform well known deep GNNs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
