Research and development of network representation learning

Ying YIN; Lixin JI; Ruiyang HUANG; Lixin DU

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

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Research and development of network representation learning

Papers | 更新时间：2024-06-03

- Research and development of network representation learning
- Chinese Journal of Network and Information Security Vol. 5, Issue 2, Pages: 77-87(2019)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2019019
  CLC： TP391.4
- Online First：2019-04，
  
  Published：15 April 2019
- 稿件说明：
移动端阅览
Ying YIN, Lixin JI, Ruiyang HUANG, et al. Research and development of network representation learning[J]. Chinese Journal of Network and Information Security, 2019, 5(2): 77-87.
DOI：

Ying YIN, Lixin JI, Ruiyang HUANG, et al. Research and development of network representation learning[J]. Chinese Journal of Network and Information Security, 2019, 5(2): 77-87. DOI： 10.11959/j.issn.2096-109x.2019019.

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。

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.

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references

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