A Survey on Signed Graph Embedding: Methods and Applications

TL;DR

The basic theories and methods of SGs are introduced and the current state of the art of signed graph embedding methods are surveyed, and the applications of different types of SG embedding methods in real-world scenarios are explored.

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.

Figures (14)

• Figure 1: According to leskovec2010signed, triads with odd positive edges are balanced (T3, T1), and triads with even positive edges (T2, T0) are unbalanced.
• Figure 2: Sociological theories on directed signed triads. In both (a) and (b), the first two triads are balanced and the last two triads are unbalanced.
• Figure 3: Illustration of Status Theory
• Figure 4: Sentiment network
• Figure 5: Example of Heterogeneous network