Multi-nodes Link Prediction Method Based on Deep Convolution Neural Networks
SHU Jian1, ZHANG Xue-pei1, LIU Lin-Lan2, YANG Zhi-yong1
1. School of Software, Nanchang Hangkong University, Nanchang, Jiangxi 330063, China;
2. School of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi 330063, China
Abstract：The current research of link prediction mainly focuses on single node pair link prediction for social network,in which the topology doesn't change frequently.In this paper,for the opportunistic network with frequent topology change,we propose a multi-nodes link prediction method based on pattern classification.This method employs chaotic time series theory to determine the slicing time of opportunistic network,and the topology of the network is depicted by the state diagram.The structural features of opportunistic network can be extracted from the evolution of the state diagram in terms of the advantages of the deep convolution neural network on the feature extraction.The evolution pattern of the future link is inferred from the current features so as to realize the multi-nodes link prediction.The experimental results on the Imote-Traces-Cambridge dataset show that the proposed method has better precision and stability than the prediction methods based on CN(Common Neighbor),Adamic-Adar and Katz.
舒坚, 张学佩, 刘琳岚, 杨志勇. 基于深度卷积神经网络的多节点间链路预测方法[J]. 电子学报, 2018, 46(12): 2970-2977.
SHU Jian, ZHANG Xue-pei, LIU Lin-Lan, YANG Zhi-yong. Multi-nodes Link Prediction Method Based on Deep Convolution Neural Networks. Acta Electronica Sinica, 2018, 46(12): 2970-2977.
[1] 熊永平,孙利民,牛建伟,刘燕.机会网络[J].软件学报,2009,20(1):124-137. XIONG Yong-ping,SUN Li-min,NIU Jian-wei,LIU Yan.Opportunistic networks[J].Journal of Software,2009,20(1):124-137.(in Chineses)
[2] 樊秀梅,单志广,张宝贤,陈辉.容迟网络体系结构及其关键技术研究[J].电子学报,2008,36(1):161-170. FAN Xiu-mei,SHAN Zhi-guang,ZHANG Bao-xian,CHEN Hui.State-of-the-art of the architecture and techniques for delay-tolerant networks[J].Acta Electronica Sinica,2008,36(1):161-170.(in Chineses)
[3] 徐方,张沪寅,王晶,徐宁,汪志勇,邓敏.基于社会上下文认知的机会路由算法[J].电子学报,2015,43(5):833-840. XU Fang,ZHANG Hu-yin,WANG Jing,XU Ning,WANG Zhi-yong,DENG Min.Social context-aware routing algorithms in opportunistic networks[J].Acta Electronica Sinica,2015,43(5):833-840.(in Chineses)
[4] 马学彬,白婧,郑田玉.机会网络中基于有权社团结构图的路由协议研究[J].电子学报,2016,44(10):2449-2458. MA Xue-bin,BAI Jing,ZHENG Tian-yu.A routing algorithm based on weighted community detection for opportunistic networks[J].Acta Electronica Sinica,2016,44(10):2449-2458.(in Chineses)
[5] 牛建伟,戴彬,孙利民,林佳骝,熊永平.PQBCF:一种基于中间中心度的机会网络P2P查询算法[J].电子学报,2013,41(9):1815-1820. NIU Jian-wei,DAI Bin,SUN Li-min,LIN Jia-liu,XIONG Yong-ping.PQBCF:a p2p query algorithm based on betweenness centrality forwarding in opportunistic networks[J].Acta Electronica Sinica,2013,41(9):1815-1820.(in Chineses)
[6] POPESCUL R,UNGAR L H.Statistical relational learning for link prediction[A].Proceedings of the Workshop on Learning Statistical Models from Relational Data at IJCAI[C].New York:ACM,2003.81-90.
[7] SCELLATO S,NOULAS A,MASCOLO C.Exploiting place features in link prediction on location-based social networks[A].Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C].San Diego:ACM,2011.1046-1054.
[8] 吕琳媛.复杂网络链路预测[J].电子科技大学学报,2010,39(5):651-661. LÜ Lin-Yuan.Link prediction on complex networks[J].Journal of University of Electronic Science and Technology of China,2010,39(5):651-661.(in Chineses)
[9] PUJARI M,KANAWATI R.Link prediction in complex networks by supervised rank aggregation[A].Proceedings of the 24th IEEE International Conference on Tools with Artificial Intelligence[C].Boca Raton:IEEE Computer Society,2012.782-789.
[10] LÜ L,PAN L,ZHOU T,ZHANG YC,STANLEY HE.Toward link predictability of complex networks[J].Proceedings of the National Academy of Sciences of the United States of America,2015,112(8):2325-2330.
[11] WANG C,SATULURI V,PARTHASARATHY S.Local probabilistic models for link prediction[A].Proceedings of the 7th IEEE International Conference on Data Mining[C].Omaha:IEEE,2007.322-331.
[12] LÜ L,ZHOU T.Link prediction in complex networks:A survey[J].Physica A Statistical Mechanics & Its Applications,2011,390(6):1150-1170.
[13] DE A,BHATTACHARYA S,SARKAR S,GANGULY N,CHAKRABARTI S.Discriminative link prediction using local,community and global signals[J].IEEE Transactions on Knowledge & Data Engineering,2016,28(8):2057-2070.
[14] CHEN Y L,CHEN M S,YU P S.Ensemble of diverse sparsifications for link prediction in large-scale networks[A].Proceedings of the 15th IEEE International Conference on Data Mining[C].Atlantic City:IEEE.2015.51-60.
[15] 马红光,李夕海,王国华,韩崇昭,许剑锋,朱小菲.相空间重构中嵌入维和时间延迟的选择[J].西安交通大学学报,2004,38(4):335-338. MA Hong-guang,LI Xi-hai,WANG Guo-hua,HAN Chong-zhao,XU Jian-feng,ZHU Xiao-fei.Selection of embedding dimension and delay time in phase space reconstruction[J].Journal of Xi'an Jiaotong University,2004,38(4):335-338.(in chinese)
[16] JAMES Scott,RICHARD Gass,JON Crowcroft,PAN Hui,CHRISTOPHE Diot,AUGUSTIN Chaintreau.CRAWDAD dataset cambridge/haggle[EB/oL].http://crawdad.org/cambridge/haggle/20090529,2009-05-29.
[17] HE K,ZHANG X,REN S,SUN J.Delving deep into rectifiers:surpassing human-level performance on imagenet classification[A].Proceedings of the 2015 IEEE International Conference on Computer Vision.LosAlamtios[C].Washington:IEEE Computer Society,2015.1026-1034.
[18] Ruder S.An overview of gradient descent optimization algorithms[OL].http://arXiv.org/abs/1609.04747,2017-6-15/2017-8-28.
[19] BENGIOY.Practical Recommendations For Gradient-Based Training Of Deep Architectures[M].Berlin,Heidelberg,Germany:Springer,2012.437-478.
[20] SRIVASTAVAN,HINTON G E,KRIZHEVSKY A,SUTSKEVER I,SALAKHUTDINOV R.Dropout:a simple way to prevent neural networks from overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958.
[21] ZAYANIM H,GAUTHIER V,SLAMA I,ZEGHLACHE D.Tracking topology dynamicity for link prediction in intermittently connected wireless networks[A].Proceedings of the 8th International Wireless Communications and Mobile Computing Conference[C].Limassol:IEEE,2012.469-474.
2018, Vol. 46 
