综述符号图嵌入方法与应用，涵盖社交、引用等网络分析
A Survey on Signed Graph Embedding: Methods and Applications
- 系统梳理符号图嵌入的核心理论与主流算法
- 覆盖异构与同构符号网络的嵌入方法与性能对比
- 适合对网络分析、图学习感兴趣的科研人员参考
符号图（Signed Graph, SG）是边带有正负或中性符号信息的图结构，在社交网络、引文网络及各类技术网络中普遍存在。针对同质与异质符号网络，已有多种网络嵌入模型被提出并发展。符号图嵌入通过学习节点的低维向量表示，支持链接预测、节点分类与社区发现等任务。本文对符号图嵌入的方法与应用进行了全面综述，介绍其基础理论与当前最新进展，并探讨不同嵌入方法在真实场景中的应用。以引文网络为例，分析作者合作网络特性。同时提供源代码与数据集，展望该领域面临的挑战与未来研究方向。
原文摘要 · Abstract (English)
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.
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