根据数据的性质，有几种类型的图表。有向图有链接方向，有符号图有正负等链接类型。有符号有向图是最复杂和信息最丰富的图。带符号有向图的图卷积尚未交付太多。尽管已经提供了许多图卷积研究，但大多数都是为无向或无符号设计的。在本文中，我们研究了一种用于有向图的谱图卷积网络。我们提出了一种新颖的复数 Hermitian 邻接矩阵，它通过复数对图信息进行编码。复数通过相位和幅度表示链路方向、符号和连通性。然后，我们用Hermitian矩阵定义了一个磁性拉普拉斯算子，并证明了它的半正定性质。最后，我们介绍了有向图卷积网络（SD-GCN）。据我们所知，这是第一个带有符号的图谱卷积。此外，与为特定图类型设计的现有卷积不同，所提出的模型具有通用性，可以应用于任何图，包括无向图、有向图或有符号图。所提出的模型的性能是用四个真实世界的图来评估的。它在链接符号预测任务中优于所有其他最先进的图卷积。或签名。所提出的模型的性能是用四个真实世界的图来评估的。它在链接符号预测任务中优于所有其他最先进的图卷积。或签名。所提出的模型的性能是用四个真实世界的图来评估的。它在链接符号预测任务中优于所有其他最先进的图卷积。 There are several types of graphs according to the nature of the data. Directed graphs have directions of links, and signed graphs have link types such as positive and negative. Signed directed graphs are the most complex and informative that have both. Graph convolutions for signed directed graphs have not been delivered much yet. Though many graph convolution studies have been provided, most are designed for undirected or unsigned. In this paper, we investigate a spectral graph convolution network for signed directed graphs. We propose a novel complex Hermitian adjacency matrix that encodes graph information via complex numbers. The complex numbers represent link direction, sign, and connectivity via the phases and magnitudes. Then, we define a magnetic Laplacian with the Hermitian matrix and prove its positive semidefinite property. Finally, we introduce Signed Directed Graph Convolution Network(SD-GCN). To the best of our knowledge, it is the first spectral convolution for graphs with signs. Moreover, unlike the existing convolutions designed for a specific graph type, the proposed model has generality that can be applied to any graphs, including undirected, directed, or signed. The performance of the proposed model was evaluated with four real-world graphs. It outperforms all the other state-of-the-art graph convolutions in the task of link sign prediction.