A signed graph (SG) is a graph where edges carry sign information attached to it. The sign of a network can be positive, negative, or neutral. A signed network is ubiquitous in a real-world network like social networks, citation networks, and various technical networks. There are many network embedding models have been proposed and developed for signed networks for both homogeneous and heterogeneous types. SG embedding learns low-dimensional vector representations for nodes of a network, which helps to do many network analysis tasks such as link prediction, node classification, and community detection. In this survey, we perform a comprehensive study of SG embedding methods and applications. We introduce here the basic theories and methods of SGs and survey the current state of the art of signed graph embedding methods. In addition, we explore the applications of different types of SG embedding methods in real-world scenarios. As an application, we have explored the citation network to analyze authorship networks. We also provide source code and datasets to give future direction. Lastly, we explore the challenges of SG embedding and forecast various future research directions in this field.

本研究解决了有符号图嵌入领域的现有方法和应用的系统评估问题。通过介绍有符号图的基本理论及当代最前沿的嵌入技术，本文提供了对有符号图嵌入的全面分析，并探讨了在现实场景中的应用。最重要的发现是，提供了源代码和数据集，为未来的研究指明了方向，同时识别了这一领域面临的挑战。

关于有符号图嵌入的调查：方法与应用