MSGNN：基于新型磁符号拉普拉斯的谱图神经网络
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
AI总结：
本文提出基于磁符号拉普拉斯的谱图神经网络MSGNN，可融合符号与方向信息，在节点聚类、链接预测等任务上表现领先，还提出新型合成模型与金融数据集。
摘要：
符号网络和有向网络在实际应用中无处不在，但针对这类网络的谱图神经网络（GNN）相关研究相对较少。本文提出一种符号有向拉普拉斯矩阵，将其命名为磁符号拉普拉斯，作为符号图上符号拉普拉斯与有向图上磁拉普拉斯的自然推广。随后利用该矩阵构建新型高效谱GNN架构，并在节点聚类和链接预测任务上开展大量实验，涵盖符号信息相关任务、方向信息相关任务以及符号与方向信息结合的任务。实验表明，所提谱GNN能有效融合符号与方向信息，在广泛数据集上达到领先性能。此外，本文还提出一种新型合成网络模型，即符号有向随机块模型，以及多个基于金融时间序列领先滞后关系的新型真实世界数据集。
英文摘要：
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.
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