网络表示学习算法综述

Survey of Network Representation Learning

网络是一系列节点和边的集合,通常表示成一个包含节点和边的图。许多复杂系统都以网络的形式来表示,如社交网络、生物网络和信息网络。为了使网络数据的处理变得简单有效,针对网络中节点的表示学习成为了近年来的研究热点。网络表示学习旨在为网络中的每个节点学习一个低维稠密的表示向量,进而可将得到的向量表示运用到常见的网络分析任务中,如节点聚类、节点分类和链路预测等。然而,绝大多数真实网络节点都有丰富的属性信息,如社交网络中的用户资料和引文网络中的文本内容。网络的属性信息对网络表示具有重要的作用,当网络高度稀疏时,网络的属性信息是网络表示重要的辅助信息,有助于更好地学习网络表示。传统的邻接矩阵仅仅表示了边的信息,而无法加入节点的属性信息。因此,网络表示不仅要保存网络的结构信息,还要保存网络的属性信息。此外,大多数真实世界网络都是动态变化的,这种变化包括网络节点的增加和减少,以及网络边的新建和消失。同时,与网络结构变化相似,网络中的属性也会随着时间的推移发生变化。随着机器学习技术的发展,针对网络表示学习问题的研究成果层出不穷,文中将针对近年来的网络表示学习方法进行系统性的介绍和总结。

A network is a collection of nodes and edges,usually is represented as a graph.Many complex systems take the form of networks,such as social networks,biological networks,and information networks.In order to make network data processing simple and effective,the representation learning for nodes in the network has become a research hotspot in recent years.Network representation learning is designed to learn a low-dimensional dense representation vector for each node in the network that can advance various learning tasks in the network analysis area such as node classification,network clustering,and link prediction.However,most of previous works have been designed only for plain networks and ignore the node attributes.When the network is high sparsity,attributes can be the very useful complementary content to help learn better representations.Therefore,the network embedding should not only preserve the structural information,but also preserve the attribute information.In addition,in practical applications,many networks are dynamic and evolve over time with the addition,changing and deletion of nodes.Meanwhile,similar as network structure,node attributes also change naturally over time.With the development of machine learning,studies on the network representation learning emerge one after another.In this paper,we will systematically introduce and summarize the network representation learning methods in recent years.