  Deep Neural Networks have shown tremendous success in the area of object
recognition, image classification and natural language processing. However,
designing optimal Neural Network architectures that can learn and output
arbitrary graphs is an ongoing research problem. The objective of this survey
is to summarize and discuss the latest advances in methods to Learn
Representations of Graph Data. We start by identifying commonly used types of
graph data and review basics of graph theory. This is followed by a discussion
of the relationships between graph kernel methods and neural networks. Next we
identify the major approaches used for learning representations of graph data
namely: Kernel approaches, Convolutional approaches, Graph neural networks
approaches, Graph embedding approaches and Probabilistic approaches. A variety
of methods under each of the approaches are discussed and the survey is
concluded with a brief discussion of the future of learning representation of
graph data.
