SigGAN : Adversarial Model for Learning Signed Relationships in Networks

TL;DR

SigGAN introduces a novel adversarial model tailored for signed network link prediction, effectively capturing positive and negative relationships while respecting structural balance constraints, outperforming existing methods.

Contribution

The paper presents SigGAN, a GAN-based approach specifically designed for signed networks, incorporating negative edge information and structural balance theory.

Findings

SigGAN outperforms state-of-the-art signed link prediction methods.

Effective handling of negative edges and class imbalance.

Validated on multiple real-world datasets.

Abstract

Signed link prediction in graphs is an important problem that has applications in diverse domains. It is a binary classification problem that predicts whether an edge between a pair of nodes is positive or negative. Existing approaches for link prediction in unsigned networks cannot be directly applied for signed link prediction due to their inherent differences. Further, additional structural constraints, like, the structural balance property of the signed networks must be considered for signed link prediction. Recent signed link prediction approaches generate node representations using either generative models or discriminative models. Inspired by the recent success of Generative Adversarial Network (GAN) based models which comprises of a discriminator and generator in several applications, we propose a Generative Adversarial Network (GAN) based model for signed networks, SigGAN. It considers the requirements of signed networks, such as, integration of information from negative edges, high imbalance in number of positive and negative edges and structural balance theory. Comparing the performance with state of the art techniques on several real-world datasets validates the effectiveness of SigGAN.