Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (12): 119-124.doi: 10.11896/jsjkx.190900027

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Network Representation Learning Method on Fusing Node Structure and Content

ZHANG Hu, ZHOU Jing-jing, GAO Hai-hui, WANG Xin   

  1. School of Computer and Information Technology Shanxi University Taiyuan 030006,China
  • Received:2019-09-09 Revised:2020-04-03 Online:2020-12-15 Published:2020-12-17
  • About author:ZHANG Hu,born in 1979Ph.Dasso-ciate professoris a member of China Computer Federation.His main research interests include Natural Language Processing and representation learning.
  • Supported by:
    National Social Science Fund of China(18BYY074),National Natural Science Foundation of China (61936012,61806117) and Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (201802012).

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Abstract: With the rapid development of neural network Technology the network representation learning method for complex network has got more and more attention.It aims to learn the low-dimensional potential representation of nodes in the network and to apply the learned characteristic representation effectively to various analysis tasks for graph data.The typical shallow random walk network representation model is mainly based on two kinds of characteristic representation methodswhich are the node structure similarity and node content similarity.Howeverthe methods can't effectively capture similar information of node structure and content at the same timeand perform poorly on the network data with the equivalent structure and content.To this endthis paper explores the fusion characteristics of node structure and node contentand proposes a representation method called SN2vecwhich is based on joint learning of unsupervised shallow neural networks.Furtherin order to validate the effectiveness of the proposed modelthis paper respectively conduct the multi-label classification and down-dimensional visualization tasks in Brazilian air-trafficAmerican air-trafficand Wikipedia datasets.The results show that the Micro-F1 of using SN2vec in multi-label classification task is better than the existing shallow random walk network representation methodsand SN2vec can also learn better potential structural representation of consistent nodes.

Key words: Complex network, Network representation learning, Random walk, Shallow neural network

CLC Number: 

  • TP391
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