论文标题
签名有向图的图形卷积
A Graph Convolution for Signed Directed Graphs
论文作者
论文摘要
签名的有向图是边缘上具有符号和方向信息的图形。尽管签名的有向图比无签名或无方向的图更有用，但分析和受到研究的关注更为复杂。本文研究了一个光谱图卷积模型，以充分利用嵌入在签名的有向边缘中的信息。我们提出了一个新型的复杂遗产邻接矩阵，该矩阵通过复数编码图形信息。与简单的基于连接的邻接矩阵相比，复杂的遗产式可以通过其相位和大小来代表边缘方向，符号和连通性。然后，我们定义了所提出的邻接矩阵的磁性laplacian，并证明它是使用光谱图卷积分析的半半明确（PSD）。我们对四个现实世界数据集进行了广泛的实验。我们的实验表明，所提出的方案的表现优于几种最先进的技术。
A signed directed graph is a graph with sign and direction information on the edges. Even though signed directed graphs are more informative than unsigned or undirected graphs, they are more complicated to analyze and have received less research attention. This paper investigates a spectral graph convolution model to fully utilize the information embedded in signed directed edges. We propose a novel complex Hermitian adjacency matrix that encodes graph information via complex numbers. Compared to a simple connection-based adjacency matrix, the complex Hermitian can represent edge direction, sign, and connectivity via its phases and magnitudes. Then, we define a magnetic Laplacian of the proposed adjacency matrix and prove that it is positive semi-definite (PSD) for the analyses using spectral graph convolution. We perform extensive experiments on four real-world datasets. Our experiments show that the proposed scheme outperforms several state-of-the-art techniques.
