网络表示学习的研究与发展

网络表示学习旨在将网络中的节点表示成低维稠密且具有一定推理能力的向量，以运用于节点分类、社区发现和链路预测等社交网络应用任务中，是连接网络原始数据和网络应用任务的桥梁。传统的网络表示学习方法都是针对网络中节点和连边只有一种类型的同质信息网络的表示学习方法，而现实世界中的网络往往是具有多种节点和连边类型的异质信息网络。而且，从时间维度上来看，网络是不断变化的。因此，网络表示学习的研究方法随着网络数据的复杂化而不断变化。对近年来针对不同网络的网络表示学习方法进行了分类介绍，并阐述了网络表示学习的应用场景。

Research and development of network representation learning

Network representation learning is a bridge between network raw data and network application tasks which aims to map nodes in the network to vectors in the low-dimensional space.These vectors can be used as input to the machine learning model for social network application tasks such as node classification,community discovery,and link prediction.The traditional network representation learning methods are based on homogeneous information network.In the real world,the network is often heterogeneous with multiple types of nodes and edges.Moreover,from the perspective of time,the network is constantly changing.Therefore,the research method of network representation learning is continuously optimized with the complexity of network data.Different kinds of network representation learning methods based on different networks were introduced and the application scenarios of network representation learning were expounded.