%0 Generic %1 zhang2018learning %A Zhang, Ziwei %A Cui, Peng %A Zhu, Wenwu %D 2018 %K deep graph learning survey %T Deep Learning on Graphs: A Survey %U http://arxiv.org/abs/1812.04202 %X Deep learning has been shown successful in a number of domains, ranging from acoustics, images to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, a significant amount of research efforts have been devoted to this area, greatly advancing graph analyzing techniques. In this survey, we comprehensively review different kinds of deep learning methods applied to graphs. We divide existing methods into three main categories: semi-supervised methods including Graph Neural Networks and Graph Convolutional Networks, unsupervised methods including Graph Autoencoders, and recent advancements including Graph Recurrent Neural Networks and Graph Reinforcement Learning. We then provide a comprehensive overview of these methods in a systematic manner following their history of developments. We also analyze the differences of these methods and how to composite different architectures. Finally, we briefly outline their applications and discuss potential future directions.

@misc{zhang2018learning, abstract = {Deep learning has been shown successful in a number of domains, ranging from acoustics, images to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, a significant amount of research efforts have been devoted to this area, greatly advancing graph analyzing techniques. In this survey, we comprehensively review different kinds of deep learning methods applied to graphs. We divide existing methods into three main categories: semi-supervised methods including Graph Neural Networks and Graph Convolutional Networks, unsupervised methods including Graph Autoencoders, and recent advancements including Graph Recurrent Neural Networks and Graph Reinforcement Learning. We then provide a comprehensive overview of these methods in a systematic manner following their history of developments. We also analyze the differences of these methods and how to composite different architectures. Finally, we briefly outline their applications and discuss potential future directions.}, added-at = {2019-06-04T11:27:46.000+0200}, author = {Zhang, Ziwei and Cui, Peng and Zhu, Wenwu}, biburl = {https://www.bibsonomy.org/bibtex/2e1f3dd6c38ef900d115a364e89cdf119/nosebrain}, description = {[1812.04202] Deep Learning on Graphs: A Survey}, interhash = {6db3412b0494fd89bfbea2834222b180}, intrahash = {e1f3dd6c38ef900d115a364e89cdf119}, keywords = {deep graph learning survey}, note = {cite arxiv:1812.04202Comment: 15 pages, 10 figures}, timestamp = {2019-06-04T11:27:46.000+0200}, title = {Deep Learning on Graphs: A Survey}, url = {http://arxiv.org/abs/1812.04202}, year = 2018 }