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.
Users
Please
log in to take part in the discussion (add own reviews or comments).