网络表示学习的研究与发展

doi:10.11959/j.issn.2096-109x.2019019

网络与信息安全学报 ›› 2019, Vol. 5 ›› Issue (2): 77-87.doi: 10.11959/j.issn.2096-109x.2019019

网络表示学习的研究与发展

尹赢¹(),吉立新¹,黄瑞阳¹,杜立新²

¹ 国家数字交换系统工程技术研究中心，河南郑州 450003
² 解放军63898部队，河南洛阳471003

修回日期:2018-12-20 出版日期:2019-04-15 发布日期:2019-04-16
作者简介:尹赢（1994- ），女，四川绵竹人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为复杂网络、网络表示学习。|吉立新（1969- ），男，江苏淮安人，国家数字交换系统工程技术研究中心研究员，主要研究方向为通信网信息安全。|黄瑞阳（1986- ），男，福建漳州人，国家数字交换系统工程技术研究中心助理研究员，主要研究方向为文本挖掘、图挖掘。|杜立新（1996- ），男，甘肃武威人，主要研究方向为大数据分析。
基金资助:
国家自然科学基金创新群体资助项目(61521003)

Research and development of network representation learning

Ying YIN¹(),Lixin JI¹,Ruiyang HUANG¹,Lixin DU²

¹ China National Digital Switching System Engineering ＆Technological Center,Zhengzhou 450003,China
² The 63898 Troop of PLA,Luoyang 471003,China

Revised:2018-12-20 Online:2019-04-15 Published:2019-04-16
Supported by:
The National Natural Science Foundation for Creative Research Groups of China(61521003)

摘要/Abstract

摘要：

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。

关键词: 大规模信息网络, 网络表示学习, 网络嵌入, 深度学习

Abstract:

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.

Key words: large-scale information network, network representation learning, network embedding, deep learning

中图分类号:

TP391.4

尹赢,吉立新,黄瑞阳,杜立新. 网络表示学习的研究与发展[J]. 网络与信息安全学报, 2019, 5(2): 77-87.

Ying YIN,Lixin JI,Ruiyang HUANG,Lixin DU. Research and development of network representation learning[J]. Chinese Journal of Network and Information Security, 2019, 5(2): 77-87.

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	算法	GreRep	HOPE	DeepWalk	Node2vec	LINE	SDNE	GraphGAN
	有向		9		9	9	9
	无向	9	9	9	9	9	9	9
网络属性
	有权	9			9	9		9
	标签
	BlogCatalog	9		9	9	9	9	9
	Flickr			9		9	9
	YouTube			9		9	9
	Wikipedia				9	9		9
实验数据集	PPI				9
	DBLP	9
	20-newsgroup	9					9
	Cora		9
	SN-Twitter		9

算法		MMDW	TADW	CENE	TriDNR
	有向	9	9		9
网络属性	有权
	文本		9	9	9
	标签	9			9
	DBLP			9	9
数据集	CiteSeer	9	9		9
	Cora	9	9
	Wiki	9	9

网络表示学习的研究与发展

Research and development of network representation learning

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