A Survey on Signed Graph Embedding: Methods and Applications

TL;DR

This survey comprehensively reviews methods for embedding signed graphs, highlighting their theoretical foundations, applications in real-world networks, and future research challenges and directions.

Contribution

It provides a thorough overview of signed graph embedding techniques, their applications, and discusses future research challenges and directions.

Findings

Survey of current state-of-the-art SG embedding methods

Application of SG embedding in citation and authorship networks

Discussion of future research challenges and directions

Abstract

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.