链路预测的方法与发展综述
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摘要
链路预测对网络结构特征的演化趋势进行挖掘有着不可磨灭的促进作用。为了对网络的未来结构变化进行预测,学者们提出了许多算法。综述了4类较为常见的链路预测方法,分别是基于节点属性、基于网络拓扑结构、基于机器学习以及基于最大似然的方法,比较了4类预测方法的优劣,并概述了几种常见的衡量链路预测算法精确度标准。最后总结并展望了链路预测的未来研究方向和发展前景。
Link prediction can promote the mining of evolutionary trends in network structure features. In order to predict links in complex networks, many algorithms have been proposed. Four types of more common link prediction methods are reviewed, including node attributes, network topology, machine learning and maximum likelihood. The advantages and disadvantages of the four types of prediction methods are compared, and several common accuracy metrics for link prediction algorithms are summarized. Finally, the future research directions and development prospects of link prediction are summarized and forecasted.
Link prediction can promote the mining of evolutionary trends in network structure features. In order to predict links in complex networks, many algorithms have been proposed. Four types of more common link prediction methods are reviewed, including node attributes, network topology, machine learning and maximum likelihood. The advantages and disadvantages of the four types of prediction methods are compared, and several common accuracy metrics for link prediction algorithms are summarized. Finally, the future research directions and development prospects of link prediction are summarized and forecasted.
引文
[1]Mohammad Al Hasan, Vineet Chaoji,Saeed Salem, et al.Link prediction using supervised learning[C]//Proceedings of SDM Workshop on Link Analysis, Counterterrorism&Security. 2006.
[2]卜心怡,陈美灵.社交网络中的链路预测研究[J].图书馆学研究,2016(17):17-21.
[3] Leskovec J,Horvitz E. Planetary-scale views on an instantmessaging network[C]//International Conference on World Wide Web. 2008:915-924.
[4] Salton G,Mcgill M J. Introduction to modern information retrieval[M]. Auckland:MuGraw-Hill, 1983.
[5] Zhou T,LüL,Zhang Y C. Predicting missing links via local information[J]. European Physical Journal B,2009,71(4):623-630.
[6]张学佩.基于3D卷积神经网络的多节点间链路预测方法研究[D].南昌:南昌航空大学,2018.
[7]张新良,郭晓迪,朱琳.基于神经网络的时滞非线性系统的广义预测控制[J].测控技术,2017,36(2):54-57.
[8] Li Y,Niu K,Tian B. Link prediction in Sina Microblog using comprehensive features and improved SVM algorithm[C]//IEEE International Conference on Cloud Computing and Intelligence Systems. 2015:18-22.
[9] Yuan G C,Murikananiah P K,Zhang Z,et al. Exploiting sentiment homophily of link-prediction[C]//Proceedings of the8th ACM Conference on Recommender System. 2014:17-24.
[10] Libent-Nowel D, Keinlberg J. The link-prediction problem of social network[M]. John Wiley&Sons,Inc. 2007.
[11] Asil A, Gürgen F. Supervised and fuzzy rule based link prediction in weighted co-authorship networks[C]//International Conference on Computer Science and Engineering.2017:407-411.
[12] Gupta A K,Sardana N. Naive Bayes approach for predicting missing links in ego networks[C]//IEEE International Symposium on Nanoelectronic and Information Systems.2017:161-165.
[13] Airoldi E M,Blei D M,Fienberg S E,et al. Mixed membership stochastic block models for relational data with appli-cation to protein-protein interactions[C]//Proceedings of the International Biometrics Society Annual Meeting. 2006,9(5):1981-2014.
[14] Huang Z, Li X, Chen H. Link prediction approach to collaborative filtering[C]//Proceedings of the 5th ACM/IEEECS Joint Conference on Digital Libraries. 2007:141-142.
[15] Menon A K,Elkan C. Link prediction via matrix factorization[C]//European Conference on Machine Learning and Knowledge Discovery in Databases. Springer-Verlag,2011:437-452.
[16]郭丽媛,王智强,梁吉业.基于边重要度的矩阵分解链路预测算法[J].模式识别与人工智能,2018,31(2):150-157.
[17] Ahmed N M,Chen L,Wang Y,et al. DeepEye:link prediction in dynamic networks based on non-negative matrix factorization[J]. Big Data Mining&Analytics,2018,1(1):19-33.
[18]刘继嘉.基于相似性演化的动态网络链路预测算法研究[D].合肥:中国科学技术大学,2018.
[19]田甜,杨艳丽,郭浩,等.基于层次随机图模型的脑网络链路预测[J].计算机应用研究,2016,33(4):1066-1069.
[2]卜心怡,陈美灵.社交网络中的链路预测研究[J].图书馆学研究,2016(17):17-21.
[3] Leskovec J,Horvitz E. Planetary-scale views on an instantmessaging network[C]//International Conference on World Wide Web. 2008:915-924.
[4] Salton G,Mcgill M J. Introduction to modern information retrieval[M]. Auckland:MuGraw-Hill, 1983.
[5] Zhou T,LüL,Zhang Y C. Predicting missing links via local information[J]. European Physical Journal B,2009,71(4):623-630.
[6]张学佩.基于3D卷积神经网络的多节点间链路预测方法研究[D].南昌:南昌航空大学,2018.
[7]张新良,郭晓迪,朱琳.基于神经网络的时滞非线性系统的广义预测控制[J].测控技术,2017,36(2):54-57.
[8] Li Y,Niu K,Tian B. Link prediction in Sina Microblog using comprehensive features and improved SVM algorithm[C]//IEEE International Conference on Cloud Computing and Intelligence Systems. 2015:18-22.
[9] Yuan G C,Murikananiah P K,Zhang Z,et al. Exploiting sentiment homophily of link-prediction[C]//Proceedings of the8th ACM Conference on Recommender System. 2014:17-24.
[10] Libent-Nowel D, Keinlberg J. The link-prediction problem of social network[M]. John Wiley&Sons,Inc. 2007.
[11] Asil A, Gürgen F. Supervised and fuzzy rule based link prediction in weighted co-authorship networks[C]//International Conference on Computer Science and Engineering.2017:407-411.
[12] Gupta A K,Sardana N. Naive Bayes approach for predicting missing links in ego networks[C]//IEEE International Symposium on Nanoelectronic and Information Systems.2017:161-165.
[13] Airoldi E M,Blei D M,Fienberg S E,et al. Mixed membership stochastic block models for relational data with appli-cation to protein-protein interactions[C]//Proceedings of the International Biometrics Society Annual Meeting. 2006,9(5):1981-2014.
[14] Huang Z, Li X, Chen H. Link prediction approach to collaborative filtering[C]//Proceedings of the 5th ACM/IEEECS Joint Conference on Digital Libraries. 2007:141-142.
[15] Menon A K,Elkan C. Link prediction via matrix factorization[C]//European Conference on Machine Learning and Knowledge Discovery in Databases. Springer-Verlag,2011:437-452.
[16]郭丽媛,王智强,梁吉业.基于边重要度的矩阵分解链路预测算法[J].模式识别与人工智能,2018,31(2):150-157.
[17] Ahmed N M,Chen L,Wang Y,et al. DeepEye:link prediction in dynamic networks based on non-negative matrix factorization[J]. Big Data Mining&Analytics,2018,1(1):19-33.
[18]刘继嘉.基于相似性演化的动态网络链路预测算法研究[D].合肥:中国科学技术大学,2018.
[19]田甜,杨艳丽,郭浩,等.基于层次随机图模型的脑网络链路预测[J].计算机应用研究,2016,33(4):1066-1069.