Matches in SemOpenAlex for { <https://semopenalex.org/work/W3125306021> ?p ?o ?g. }
- W3125306021 abstract "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." @default.
- W3125306021 created "2021-02-01" @default.
- W3125306021 creator A5037191678 @default.
- W3125306021 creator A5063512925 @default.
- W3125306021 creator A5063795209 @default.
- W3125306021 creator A5069082023 @default.
- W3125306021 date "2020-12-31" @default.
- W3125306021 modified "2023-09-27" @default.
- W3125306021 title "Deep Graph Generators: A Survey" @default.
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