  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.
