论文标题
MSGNN：基于新型磁性Laplacian的光谱图神经网络
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
论文作者
论文摘要
在现实世界中，签名和定向网络无处不在。但是，对于此类网络，较少的工作提出了频谱图神经网络（GNN）。在这里，我们介绍了一个签名的定向拉普拉斯矩阵，我们将其称为磁性签名的laplacian，作为在签名的图表上签名的laplacian的自然概括，在有向图上的磁Laplacian。然后，我们使用此矩阵来构建一种新型的有效光谱GNN结构，并在节点簇和链接预测任务上进行广泛的实验。在这些实验中，我们考虑了与签名信息有关的任务，与定向信息相关的任务以及与签名和定向信息有关的任务。我们证明，我们提出的光谱GNN有效地合并了签名和定向信息，并在广泛的数据集中获得领先的性能。此外，我们提供了一种新颖的合成网络模型，我们将其称为签名的定向随机块模型，以及基于财务时间序列中铅滞后关系的许多新颖的现实世界数据集。
Signed and directed networks are ubiquitous in real-world applications. However, there has been relatively little work proposing spectral graph neural networks (GNNs) for such networks. Here we introduce a signed directed Laplacian matrix, which we call the magnetic signed Laplacian, as a natural generalization of both the signed Laplacian on signed graphs and the magnetic Laplacian on directed graphs. We then use this matrix to construct a novel efficient spectral GNN architecture and conduct extensive experiments on both node clustering and link prediction tasks. In these experiments, we consider tasks related to signed information, tasks related to directional information, and tasks related to both signed and directional information. We demonstrate that our proposed spectral GNN is effective for incorporating both signed and directional information, and attains leading performance on a wide range of data sets. Additionally, we provide a novel synthetic network model, which we refer to as the Signed Directed Stochastic Block Model, and a number of novel real-world data sets based on lead-lag relationships in financial time series.
