图神经架构搜索综述

Graph data can generally model the complex relationships between entities. From as small as the molecular and amino acid structures in proteins, to as large as the worldwide logistics and transportation networks; from the social networks in the human society to the Internet in the information space, all these data can be uniformly represented as graphs. Huge research and application values exist underlying the graph data. Graph neural networks are the dominant paradigm for machine learning on graphs over the past few years. By redefining neural network architectures on the link relationship of graph data and realizing end-to-end learning paradigms, graph neural networks can effectively handle a variety of graph analytical and mining tasks such as node classification, link prediction, and graph classification. However, due to the complexity of graph data, the diversity of graph tasks, and the complex architecture of graph neural networks, it becomes increasingly difficult to manually design the optimal graph neural network architectures, failing to adapt to open and changing environments. Graph neural architecture search, which aims to automate the design of optimal graph neural network architectures for specific data sets and tasks, comes into being and has attracted considerable attention from both academia and industry. In this paper, we provide a comprehensive review for the rapidly evolving and emerging field of graph neural architecture search. In particular, we systematically review and summarize more than 40 published graph neural architecture search algorithms, and comprehensively classify, compare and comment on the existing algorithms from the search space, the search strategy, the model evaluation strategy, and other characteristics of the models. We also summarize the above algorithms from the experimental point of view. In addition, we analyze recent trends in graph neural architecture search studies. Finally, we share our insights on the future research direction of graph neural architecture search.