Computer Science ›› 2020, Vol. 47 ›› Issue (4): 233-237.doi: 10.11896/jsjkx.190600151

• Computer Network • Previous Articles     Next Articles

Improved SDNE in Weighted Directed Network

MA Yang, CHENG Guang-quan, LIANG Xing-xing, LI Yan, YANG Yu-ling, LIU Zhong   

  1. College of Systems Engineering,National University of Defense Technology,Changsha 410073,China
  • Received:2019-06-26 Online:2020-04-15 Published:2020-04-15
  • Contact: CHENG Guang-quan,born in 1982,Ph.D,is a member of China Computer Federation (CCF).His main research interests include network analysis and machine learning.
  • About author:MA Yang,born in 1993,postgraduate.His main research interests include link prediction and graph neural networks.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61201328,71471175).

Abstract: The data form of network can express the entity and the relation between entity and entity.Network structure is common in the real world.It is great significance to study the relationship between nodes and edges in networks.Network representation technology transforms the structure information of network into node vector,which can reduce the complexity of graph representation,and can be effectively applied to tasks such as classification,network reconstruction and link prediction.The SDNE (structural deep network embedding) algorithm proposed in recent years has made outstanding achievements in the field of graph auto-encoder.In view of the limitations of SDNE in weighted and directed networks,this paper proposed a new network representation model based on graph auto-encoder from the perspectives of network structure and measurement index.The concepts of receiving and sending vector are introduced to optimize the decoding part of the neural network,which reduce the para-meters of the network to speed up the convergence speed.This paper proposed a measurement index based on the node degree,and reflected the weighted characteristics of the network in the results of the network representation.Experiments on three directed weighted datasets show that the proposed method can achieve better results than the traditional method and the original SDNE method in network reconstruction and link prediction tasks.

Key words: Complex network, Network representation, Network reconstruction, Link prediction, Auto-encoder

CLC Number: 

  • TP311
[1]LV L Y.Link Prediction on Complex Networks[J].Journal of University of Electronic Science and Technology of China,2010,39(5):651-661.
[2]LV L,ZHOU T.Link prediction in complex networks:A survey[J].Physica A Statistical Mechanics & Its Applications,2010,390(6):1150-1170.
[3]MA Y,LIANG X,HUANG J,et al.Intercity TransportationConstruction Based on Link Prediction[C]//2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).IEEE,2017.
[4]KLIMT B.The Enron corpus:A new dataset for email classification research[C]//Proc.15th European Conf.MachineLear-ning.2004.
[5]TU C C,YANG C,LIU Z Y,et al.Network representationlearning:an overview[J].Scientia Sinical Informationis,2017(8):32-48.
[6]HAMILTON W L,YING R,LESKOVEC J.RepresentationLearning on Graphs:Methods and Applications[J].arxiv:1709.05584.
[7]GOYAL P,FERRARA E.Graph Embedding Techniques,Applications,and Performance:A Survey[J].arXiv:1705.02801.
[8]BELKIN M.Laplacian eigenmaps and spectral techniques forembedding and clustering[J].Advances in neural information processing systems,2002,14(6):585-591.
[9]ADITYA GROVER J L.node2vec:Scalable Feature Learningfor Networks[J].arXiv:1607.00653.
[10]GOLDBERG Y.A Primer on Neural Network Models for Natural Language Processing[J].arXiv:1510.00726,2015.
[11]WANG D,PENG C,ZHU W.Structural Deep Network Embedding[C]//Acm Sigkdd International Conference on Knowledge Discovery & Data Mining.2016.
[12]SAMI A,PEROZZI B,AL-RFOU R.Learning Edge Representations via Low-Rank Asymmetric Projections[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management-(CIKM 17).2017:1787-1796.
[13]GOYAL P,KAMRA N,HE X,et al.DynGEM:Deep Embedding Method for Dynamic Graphs[J].arXiv:1805.11273.
[14]SUTSKEVER I,VINYALS O,LE Q V.Sequence to Sequence Learning with Neural Networks[J].arXiv:1511.04868.
[15]KIM J,PARK H,LEE J E,et al.SIDE:Representation Learning in Signed Directed Networks[C]//the 2018 World Wide Web Conference.2018.
[16]CHEN H,PEROZZI B,AL-RFOU R,et al.A Tutorial on Network Embeddings[J].arXiv:1808.02590.
[17]CUNCHAO T,XIANGKAI Z,HAO W,et al.A Unified Framework for Community Detection and Network Representation Learning[J].IEEE Transactions on Knowledge and Data Engineering,2019,31(6):1051-1065.
[18]GOYAL P,HOSSEINMARDI H,FERRARA E,et al.Capturing Edge Attributes via Network Embedding[J].IEEE Tran-sactions on Computational Social Systems,2018,5(4):907-917.
[1] DING Yu, WEI Hao, PAN Zhi-song, LIU Xin. Survey of Network Representation Learning [J]. Computer Science, 2020, 47(9): 52-59.
[2] YANG Chao, LIU Zhi. Study on Complex Network Cascading Failure Based on Totally Asymmetric Simple Exclusion Process Model [J]. Computer Science, 2020, 47(9): 265-269.
[3] ZHANG Meng-yue, HU Jun, YAN Guan, LI Hui-jia. Analysis of China’s Patent Application Concern Based on Visibility Graph Network [J]. Computer Science, 2020, 47(8): 189-194.
[4] ZHANG Qing-qi, LIU Man-dan. Multi-objective Five-elements Cycle Optimization Algorithm for Complex Network Community Discovery [J]. Computer Science, 2020, 47(8): 284-290.
[5] WANG Hui, LE Zi-chun, GONG Xuan, WU Yu-kun, ZUO Hao. Review of Link Prediction Methods Based on Feature Classification [J]. Computer Science, 2020, 47(8): 302-312.
[6] JIANG Zong-li, LI Miao-miao, ZHANG Jin-li. Graph Convolution of Fusion Meta-path Based Heterogeneous Network Representation Learning [J]. Computer Science, 2020, 47(7): 231-235.
[7] HUANG Yi, SHEN Guo-wei, ZHAO Wen-bo, GUO Chun. Network Representation Learning Algorithm Based on Vulnerability Threat Schema [J]. Computer Science, 2020, 47(7): 292-298.
[8] DONG Ming-gang, GONG Jia-ming and JING Chao. Multi-obJective Evolutionary Algorithm Based on Community Detection Spectral Clustering [J]. Computer Science, 2020, 47(6A): 461-466.
[9] FU Kun, QIU Qian, ZHAO Xiao-meng, GAO Jin-hui. Event Detection Method Based on Node Evolution Staged Optimization [J]. Computer Science, 2020, 47(5): 96-102.
[10] YUAN Rong, SONG Yu-rong, MENG Fan-rong. Link Prediction Method Based on Weighted Network Topology Weight [J]. Computer Science, 2020, 47(5): 265-270.
[11] LI Gang, WANG Chao, HAN De-peng, LIU Qiang-wei, LI Ying. Study on Multimodal Image Genetic Data Based on Deep Principal Correlated Auto-encoders [J]. Computer Science, 2020, 47(4): 60-66.
[12] LI Xin-chao, LI Pei-feng, ZHU Qiao-ming. Knowledge Graph Representation Based on Improved Vector Projection Distance [J]. Computer Science, 2020, 47(4): 189-193.
[13] LIU Miao-miao,HU Qing-cui,GUO Jing-feng,CHEN Jing. Survey of Link Prediction Algorithms in Signed Networks [J]. Computer Science, 2020, 47(2): 21-30.
[14] LUO Yue-tong,BIAN Jing-shuai,ZHANG Meng,RAO Yong-ming,YAN Feng. Detection Method of Chip Surface Weak Defect Based on Convolution Denoising Auto-encoders [J]. Computer Science, 2020, 47(2): 118-125.
[15] ZHANG Hu, ZHOU Jing-jing, GAO Hai-hui, WANG Xin. Network Representation Learning Method on Fusing Node Structure and Content [J]. Computer Science, 2020, 47(12): 119-124.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .