Generative Adversarial Network (GAN) in Social Network: Introduction, Applications, Challenges and Future Directions.

Generative adversarial networks are now a hot topic in the field of artificial intelligence. GANs take their cue by including a generator and a discriminator learned using the adversarial learning concept. Specifically, GANs seek to anticipate the distribution of the information samples from the real world and then generate a new response based on this prediction. Since their inception, GANs have been the subject of extensive research in different fields of social networks, such as image, vision, speech processing, and language processing. This survey paper summarises GAN-based models' theoretic concept, applications, advantages, and disadvantages with development trends and potential future directions. We elaborate on the topic by discussing the applications of GAN and the challenges that must be addressed. In particular, we discuss speech and language processing, image processing, face detection, texture-transferring, and communication-based GAN applications. We conclude our findings with a discussion of potential future directions.