计算机科学
嵌入
人工智能
代表(政治)
节点(物理)
特征学习
卷积神经网络
机器学习
网络体系结构
任务(项目管理)
理论计算机科学
计算机安全
管理
结构工程
政治
政治学
法学
经济
工程类
作者
Zhongying Zhao,Hui Zhou,Liang Qi,Liang Chang,MengChu Zhou
出处
期刊:IEEE Transactions on Network Science and Engineering
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:8 (1): 695-706
被引量:30
标识
DOI:10.1109/tnse.2020.3048902
摘要
Network embedding aims to map a complex network into a low-dimensional vector space while maximally preserving the properties of the original network. An attributed network is a typical real-world network that models the relationships and attributes of real-world entities. Its analysis is of great significance in many applications. However, most such networks are incomplete with partially-known attributes, links and labels. Traditional network embedding methods are designed for a complete network and cannot be applied to a network with incomplete information. Thus, this work proposes an inductive embedding model to learn the robust representations for a partially-unseen attributed network. It is designed based on a multi-core convolutional neural network and a semi-supervised learning mechanism, which can preserve the properties of such a network and generate the effective representations for unseen nodes in a model training process. We evaluate its performance on the task of inductive node classification and community detection via three real-world attributed networks. Experimental results show that it significantly outperforms the state-of-the-art.
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