Computer Science ›› 2020, Vol. 47 ›› Issue (7): 231-235.doi: 10.11896/jsjkx.190600085

• Computer Network • Previous Articles     Next Articles

Graph Convolution of Fusion Meta-path Based Heterogeneous Network Representation Learning

JIANG Zong-li, LI Miao-miao, ZHANG Jin-li   

  1. Department of Information Technology,Beijing University of Technology,Beijing 100124,China
  • Received:2019-06-17 Online:2020-07-15 Published:2020-07-16
  • About author:JIANG Zong-li,born in 1956,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include network information search and processing.
    LI Miao-miao,born in 1994,postgra-duate.Her main research interests include network representation learning.

Abstract: In recent years,network representation learning has received more and more attention as an effective method for analyzing heterogeneous information networks by representing nodes in a low-dimensional space.Random walk based methods are currently popular methods to learn network embedding,however,most of these methods are based on shallow neural networks,which make it difficult to capture heterogeneous network structure information.The graph convolutional network (GCN) is a popular method for deep learning of graphs,which is known to be capable of better exploitation of network topology,but current design of GCN is intended for homogenous networks,ignoring the rich semantic information in the network.In order to effectively mine the semantic information and highly nonlinear network structure information in heterogeneous information networks,this paper proposes a heterogeneous network representation learning algorithm based on graph convolution of fusion meta-path(MG2vec)to improve the effect of network representation.Firstly,the algorithm obtains rich semantic information in heterogeneous information networks through relevance measurement based on meta-paths.Then the graph convolution network is used for deep learning to capture the characteristics of nodes and neighbor nodes,to make up for the deficiency of shallow model in capturing the information of the network structure,so as to better integrate rich semantic information and structural information into the low-dimensional node representation.Experiments are carried out on DBLP and IMDB,compared with DeepWalk,node2vec and Metapath2vec classical algorithms,the proposed MG2vec algorithm has higher classification accuracy and better performance in multi-label classification tasks,the precision and Macro-F1 value can be respectively up to 94.49% and 94.16%,and the both of values are up to 26.05% and 28.73% higher respectively than DeepWalk.The experimental results show that the performance of MG2vec algorithm is better than that of classical network representation learning algorithms,and MG2vec has better heterogeneous information network representation effect.

Key words: Network representation learning, Heterogeneous information network, Meta-path, Semantics information, Network structure information, Graph convolutional networks

CLC Number: 

  • TP183
[1] TU C C,YANG C,LIU Z Y,et al.Network representation learning:an overview [J].Scientia Sinica Informations,2017,47(8):980-996.
[2] SHEIKH N,KEFATO Z T,MONTRESOR A.Semi-Supervised Heterogeneous Information Network Embedding for Node Classification using 1D-CNN[C]//2018 Fifth International Confe-rence on Social Networks Analysis,Management and Security (SNAMS).IEEE,2018:177-181.
[3] YIN Y,JI L X,HUANG R Y,et al.Research and development of network representation learning[J].Chinese Journal of Network and Information Security,2019,5(2):77-87.
[4] JIANG Z L,ZHANG J L,DU Y P,et al.Hierarchical construction and node classification of heterogeneous network based on stacked denoising autoencoder[J].Journal of Beijing University of Technology,2018,44(9):1217-1226.
[5] DONG Y,CHAWLA N V,SWAMI A.metapath2vec:Scalable representation learning for heterogeneous networks[C]//Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining.ACM,2017:135-144.
[6] DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering[C]//Advances in neural information processing systems.2016:3844-3852.
[7] KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.
[8] ZHANG D,YIN J,ZHU X,et al.Network representation lear-ning:A survey[J].IEEE transactions on Big Data,2017,PP(99):1-1.
[9] PEROZZI B,AlRFOU R,SKIENA S.Deepwalk:Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining.ACM,2014:701-710.
[10] MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]//Advances in neural information processing systems.2013:3111-3119.
[11] MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].arXiv:1301.3781,2013.
[12] TANG J,QU M,WANG M,et al.Line:Large-scale information network embedding[C]//Proceedings of the 24th international conference on world wide web.International World Wide Web Conferences Steering Committee,2015:1067-1077.
[13] GROVER A,LESKOVEC J.node2vec:Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining.ACM,2016:855-864.
[14] LE C Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444.
[15] ZHANG J,JIANG Z,LI T.CHIN:Classification with META-PATH in Heterogeneous Information Networks[C]//International Conference on Applied Informatics.Springer,Cham,2018:63-74.
[16] SHI C,LI Y,ZHANG J,et al.A survey of heterogeneous information network analysis[J].IEEE Transactions on Knowledge and Data Engineering,2016,29(1):17-37.
[17] HUANG Z,ZHENG Y,CHENG R,et al.Meta structure:Computing relevance in large heterogeneous information networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2016:1595-1604.
[18] GUPTA M,KUMAR P,BHASKER B.A new relevance mea-sure for heterogeneous networks[C]//International Conference on Big Data Analytics and Knowledge Discovery.Cham:Sprin-ger,2015:165-177.
[19] SEBASTIANI F.Machine learning in automated text categorization[J].ACM computing surveys (CSUR),2002,34(1):1-4.
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