图神经网络在复杂图挖掘上的研究进展

Progress of Graph Neural Networks on Complex Graph Mining

图神经网络对非欧式空间数据建立了深度学习框架,相比传统网络表示学习模型,它对图结构能够实施更加深层的信息聚合操作.近年来,图神经网络完成了向复杂图结构的迁移,诞生了一系列基于复杂图的图神经网络模型.然而,现有综述文章缺乏对复杂图神经网络全面、系统的归纳和总结工作.将复杂图分为异质图、动态图和超图3种类型.将异质图神经网络按照信息聚合方式划分为关系类型感知和元路径感知两大类,在此基础上,分别介绍普通异质图和知识图谱.将动态图神经网络按照处理时序信息的方式划分成基于循环神经网络、基于自编码器以及时空图神经网络三大类.将超图神经网络按照是否将超图展开成成对图划分为展开型和非展开型两大类,进一步按照展开方式将展开型划分成星形展开、团式展开和线形展开3种类型.详细阐述了每种算法的核心思想,比较了不同算法间的优缺点,系统列举了各类复杂图神经网络的关键算法、(交叉)应用领域和常用数据集,并对未来可能的研究方向进行了展望.

Graph neural networks(GNNs)establish a deep learning framework for non-Euclidean spatial data.Compared with traditional network embedding methods,they perform deeper aggregating operations on graph structures.In recent years,GNNs have been extended to complex graphs.Nevertheless,there lacks qualified surveys which give comprehensive and systematic classification and summary on GNNs based on complex graphs.This study divides the complex graphs into 3 categories,i.e.,heterogeneous graphs,dynamic graphs,and hypergraphs.GNNs based on heterogeneous graphs are divided into 2 types,i.e.,edge-type aware and meta-path aware,according to the procedure that the information is aggregated.Dynamic GNNs graphs are divided into three categories:RNN-based methods,autoencoderbased methods,and spatio-temporal graph neural networks.Hypergraph GNNs are divided into expansion methods and non-expansion methods,and the expansion methods are further divided into star-expansion,clique-expansion,and line-expansion according to the expansion mode they use.The core idea of every method is illustrated in detail,the advantages and disadvantages of different algorithms are compared,the key procedures,(cross)application fields,and commonly used data sets of different complex graph GNNs are systematically listed,and some possible research directions are proposed.