Deep Graph Generators: A Survey
This survey provides a comprehensive overview of deep graph generators for researchers and practitioners interested in this emerging field, helping them navigate existing methods and identify future research avenues.
This paper surveys deep learning-based graph generation approaches, classifying them into five categories: autoregressive, autoencoder-based, RL-based, adversarial, and flow-based generators. It provides detailed descriptions, lists public source codes, datasets, and evaluation metrics, and discusses challenges and future directions.
Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years. Thanks to the advances in graph-based deep learning, and in particular graph representation learning, deep graph generation methods have recently emerged with new applications ranging from discovering novel molecular structures to modeling social networks. This paper conducts a comprehensive survey on deep learning-based graph generation approaches and classifies them into five broad categories, namely, autoregressive, autoencoder-based, RL-based, adversarial, and flow-based graph generators, providing the readers a detailed description of the methods in each class. We also present publicly available source codes, commonly used datasets, and the most widely utilized evaluation metrics. Finally, we highlight the existing challenges and discuss future research directions.