计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 212-218.doi: 10.11896/jsjkx.201000015
富坤1, 赵晓梦1, 付紫桐2, 高金辉1, 马浩然3
FU Kun1, ZHAO Xiao-meng1, FU Zi-tong2, GAO Jin-hui1, MA Hao-ran3
摘要： 网络表示学习的目标是将网络中的节点嵌入到低维的向量空间,为下游任务提供有效特征表示。在现实场景中,大规模网络通常具有不完整的链路,而现有的大多数网络表示学习模型都是在网络是完整的假设下设计的,因此其性能很容易受到链路缺失的影响。针对该问题,文中提出了一种基于不完全信息的深度网络表示学习方法DNRL(Deep Network Representation Learning)。首先采用转移概率矩阵将结构信息和属性信息进行动态融合,弥补了结构信息不完整带来的过大损失,然后采用一种具有强大特征提取能力的深度生成模型(变分自编码器)来学习节点的低维表示,并捕获网络数据中潜在的高非线性特征。在3个真实属性网络上的实验结果表明,与当前常用的网络表示学习模型相比,所提模型在不同程度链路缺失的节点分类任务中都明显地改善了分类效果,在可视化任务中更清晰地反映了节点的团簇关系。
中图分类号:
| [1]TU C C,YANG C,LIU Z Y,et al.Network representation learning:an overview[J].Scientia Sinica Information,2017,47(8):980-996. [2]ZHANG D K,YIN J,ZHU X Q,et al.Network representation learning:A survey[J].IEEE Transactions on Big Data,2017(99):1. [3]DING Y,WEI H,PAN Z S,et al.Survey of Network Representation Learning[J].Computer Science,2020,47(9):52-59. [4]TANG J,AGGARWAL C,LIU H.Node classification signed social networks[C]//Proceedings of the 2016 SIAM International Conference on Data Mining.Society for Industrial and Applied Mathematics,2016:54-62. [5]TANG J,LIU J,ZHANG M,et al.Visualizing Large-scale and High-dimensional Data[C]//Proceedings of the 25th International Conference on World Wide Web.2016:287-297. [6]WANG H,ZHANG F,HOU M,et al.Shine:Signed heteroge- neous information network embedding for sentiment link prediction[C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining.2018:592-600. [7]HAN N,QIAO S J,YUAN C A,et al.A Fast Parallel Community Detection Algorithm for Mobile Social Networks[J].Journal of Chongqing University of Technology(Natural Science),2020,34(1):94-102. [8]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. [9]WANG J.Locally linear embedding [M]//Geometric Structure of High-Dimensional Data and Dimensionality Reduction.Berlin:Springer,2012:203-220. [10]BELKIN M,NIYOGI P.Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering[C]//Advances in Neural Information Processing Systems.2002:585-591. [11]PEROZZI B,AI-RFOU R,SKIENA S.DeepWalk:Online Lear- ning of Social Representations[C]//The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.ACM,2014:701-710. [12]GROVER A,LESKOVEC J.Node2vec:Scalable Feature Lear- ning for Networks[C]//The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2016:855-864. [13]CAO S S,LU W,XU Q K.Deep neural networks for learning graph representations[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence.AAAI Press,2016:1145-1152. [14]WANG D X,CUI P,ZHU W W.Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM Press,2016:1225-1234. [15]YANG C,LIU Z Y,ZHAO D L,et al.Network representation learning with rich text information[C]//Proceedings of International Joint Conference on Artificial Intelligence.2015:2111-2117. [16]HUANG X,LI J D,HU X.Accelerated attributed network embedding[C]//Proceedings of the 2017 SIAM International Conference on Data Mining.Philadelphia,PA:SIAM,2017:633-641. [17]LIAO L Z,HE X N,ZHANG H W,et al.Attributed social network embedding[J].IEEE Transactions on Knowledge and Data Engineering,2018,30(12):2257-2270. [18]SMITH J A,MOODY J,MORGAN J H.Network sampling coverage II:The effect of non-random missing data on network measurement[J].Social Networks,2017,48:78-99. [19]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.New York:ACM,2015:1067-1077. [20]CAO S S,LU W,XU Q K.Grarep:Learning graph representations with global structural information[C]//Proceedings of the 24th ACM International Conference on Information and Know-ledge Management.New York:ACM,2015:891-900. [21]LIANG J,JACOBS P,SUN J,et al.Semi-supervised embedding in attributed networks with outliers[C]//Proceedings of the 2018 SIAM International Conference on Data Mining.Philadelphia:SIAM,2018:153-161. [22]BANDYOPADHYAY S,LOKESH N,MURTY M N.Outlier Aware Network Embedding for Attributed Networks[J].Proceedings of the AAAI Conference on Artificial Intelligence,2019,33:12-19. [23]ZHANG D K,YIN J,ZHU X Q,et al.SINE:Scalable Incomplete Network Embedding[C]//IEEE International Conference on Data Mining.Washington DC,USA:IEEE Computer Society,2018:737-746. [24]HOU C,HE S,TANG K.Attributed Network Embedding for Incomplete Structure Information[J].arXiv:1811.11728,2018. [25]BOUMA G.Normalized(pointwise) mutual information in collocation extraction[J].Proceedings of GSCL,2009,30:31-40. [26]YUAN F N,ZHANG L,SHI J T,et al.Theories and Application of Auto-Encoder Neural Networks:A Literature Survey[J].Chinese Journal of Computers,2019,42(1):203-230. [27]REZENDE D J,MOHAMED S.Variational inference with normalizing flows[C]//International Conference on Machine Learning.PMLR,2015:1530-1538. [28]LI Y,PAN Q,WANG S H,et al.Disentangled Variational Auto-Encoder for semi-supervised learning[J].Information Sciences,2019,482(5):73-85. [29]YANG D J,WANG S Z,LI C Z,et al.From properties to links:Deep network embedding on incomplete graphs[C]//Procee-dings of the 2017 ACM on Conference on Information and Knowledge Management.ACM,2017:367-376. |
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