计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 231-235.doi: 10.11896/jsjkx.190600085
蒋宗礼, 李苗苗, 张津丽
JIANG Zong-li, LI Miao-miao, ZHANG Jin-li
摘要: 近年来,网络表示学习(Network Representation Learning,NRL)作为一种在低维空间中表示节点来分析异质信息网络(Heterogeneous Information Networks,HIN)的有效方法受到越来越多的关注。基于随机游走的方法是目前网络表示学习常用的方法,然而这些方法大多基于浅层神经网络,难以捕获异质网络结构信息。图卷积神经网络(Gragh Convolutional Network,GCN)是一种流行的能对图进行深度学习的方法,能够更好地利用网络拓扑结构,但目前的GCN设计针对的是同质信息网络,忽略了网络中丰富的语义信息。为了有效地挖掘异质信息网络中的语义信息和高度非线性的网络结构信息,进而提高网络表示的效果,文中提出了一种基于融合元路径的图卷积异质网络表示学习算法(MG2vec)。该算法首先通过基于元路径的关联度量方法来获取异质信息网络中丰富的语义信息;然后采用图卷积神经网络进行深度学习,捕捉节点和邻居节点的特征,弥补浅层模型捕捉网络结构信息能力不足的缺陷,从而实现将丰富的语义信息和结构信息更好地融入低维的节点表示中。在数据集DBLP和IMDB上分别进行实验,相比DeepWalk,node2vec和Metapath2vec算法,所提MG2vec算法在多标签分类任务上的分类精确率更高且性能更优,精确率和Macro-F1值分别达到了94.49%和94.16%,且与DeepWalk相比分别最高提升了26.05%和28.73%。实验结果证明,MG2vec算法的性能优于经典的网络表示学习算法,具有更好的异质信息网络表示效果。
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[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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