  The online social platforms, like Twitter, Facebook, LinkedIn and WeChat,
have grown really fast in last decade and have been one of the most effective
platforms for people to communicate and share information with each other. Due
to the "word of mouth" effects, information usually can spread rapidly on these
social media platforms. Therefore, it is important to study the mechanisms
driving the information diffusion and quantify the consequence of information
spread. A lot of efforts have been focused on this problem to help us better
understand and achieve higher performance in viral marketing and advertising.
On the other hand, the development of neural networks has blossomed in the last
few years, leading to a large number of graph representation learning (GRL)
models. Compared to traditional models, GRL methods are often shown to be more
effective. In this paper, we present a comprehensive review for existing works
using GRL methods for popularity prediction problem, and categorize related
literatures into two big classes, according to their mainly used model and
techniques: embedding-based methods and deep learning methods. Deep learning
method is further classified into six small classes: convolutional neural
networks, graph convolutional networks, graph attention networks, graph neural
networks, recurrent neural networks, and reinforcement learning. We compare the
performance of these different models and discuss their strengths and
limitations. Finally, we outline the challenges and future chances for
popularity prediction problem.
