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Introduction
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Community-Based Feature Generation Methods
Result Analysis
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Computer Science
Information Systems

Community Enhanced Link Prediction in Dynamic Networks

Profile image of Shashank Sheshar SinghShashank Sheshar Singh

ACM Transactions on the Web

https://doi.org/10.1145/3580513
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32 pages

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Abstract

The growing popularity of online social networks is quite evident nowadays and provides an opportunity to allow researchers in finding solutions for various practical applications. Link prediction is the technique of understanding network structure and identifying missing and future links in social networks. One of the well-known classes of methods in link prediction is a similarity-based method, which uses local and global topological information of the network to predict missing links. Some methods also exist based on quasi-local features to achieve a trade-off between local and global information on static networks. These quasi-local similarity-based methods are not best suited for considering community information in dynamic networks, failing to balance accuracy and efficiency. Therefore, a community enhanced framework is presented in this paper to predict missing links on dynamic social networks. First, a link prediction framework is presented to predict missing links using par...

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References (96)

  1. Sisay Fissaha Adafre and Maarten de Rijke. 2005. Discovering Missing Links in Wikipedia. In Proceedings of the 3rd International Workshop on Link Discovery (Chicago, Illinois) (LinkKDD 2005). Association for Computing Machinery, New York, NY, USA, 90ś97. https://doi.org/10.1145/1134271.1134284
  2. Lada A. Adamic and Natalie Glance. 2005. The Political Blogosphere and the 2004 U.S. Election: Divided They Blog. In Proceedings of the 3rd International Workshop on Link Discovery (Chicago, Illinois) (LinkKDD 2005). ACM, New York, NY, USA, 36ś43. https: //doi.org/10.1145/1134271.1134277
  3. Gaurav Agarwal and David Kempe. 2008. Modularity-maximizing graph communities via mathematical programming. The European Physical Journal B 66, 3 (2008), 409ś418. https://doi.org/10.1140/epjb/e2008-00425-1
  4. Nahla Mohamed Ahmed, Ling Chen, Yulong Wang, Bin Li, Yun Li, and Wei Liu. 2018. DeepEye: Link prediction in dynamic networks based on non-negative matrix factorization. Big Data Mining and Analytics 1, 1 (2018), 19ś33. https://doi.org/10.26599/BDMA.2017.9020002
  5. Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, and Eric P. Xing. 2008. Mixed Membership Stochastic Blockmodels. J. Mach. Learn. Res. 9 (jun 2008), 1981ś2014.
  6. Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, and Mohammed Zaki. 2006. Link prediction using supervised learning. In SDM06: workshop on link analysis, counter-terrorism and security, Vol. 30. 798ś805. https://arxiv.org/abs/2006.16327
  7. Massimo Aria, Corrado Cuccurullo, and Agostino Gnasso. 2021. A comparison among interpretative proposals for Random Forests. Machine Learning with Applications 6 (2021), 100094. https://doi.org/10.1016/j.mlwa.2021.100094
  8. Suresh Balakrishnama and Aravind Ganapathiraju. 1998. Linear discriminant analysis-a brief tutorial. Institute for Signal and information Processing 18, 1998 (1998), 1ś8.
  9. Albert-Laszlo Barabâsi, Hawoong Jeong, Zoltan Néda, Erzsebet Ravasz, Andras Schubert, and Tamas Vicsek. 2002. Evolution of the social network of scientiic collaborations. Physica A: Statistical mechanics and its applications 311, 3-4 (2002), 590ś614. https: //doi.org/10.1016/S0378-4371(02)00736-7
  10. RICHARD BELLMAN. 1958. ON A ROUTING PROBLEM. Quart. Appl. Math. 16, 1 (1958), 87ś90. http://www.jstor.org/stable/43634538
  11. Gérard Biau. 2012. Analysis of a Random Forests Model. J. Mach. Learn. Res. 13, null (apr 2012), 1063ś1095. https://doi.org/10.5555/ 2188385.2343682
  12. Catherine A. Bliss, Morgan R. Frank, Christopher M. Danforth, and Peter Sheridan Dodds. 2014. An evolutionary algorithm approach to link prediction in dynamic social networks. Journal of Computational Science 5, 5 (2014), 750ś764. https://doi.org/10.1016/j.jocs.2014.01. 003
  13. Pavel Chebotarev and Elena Shamis. 2006. The Matrix-Forest Theorem and Measuring Relations in Small Social Groups. CoRR abs/math/0602070 (2006). https://doi.org/10.48550/arXiv.math/0602070
  14. Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD 2016). Association for Computing Machinery, New York, NY, USA, 785ś794. https://doi.org/10.1145/2939672.2939785
  15. Carter Chiu and Justin Zhan. 2018. Deep Learning for Link Prediction in Dynamic Networks Using Weak Estimators. IEEE Access 6 (2018), 35937ś35945. https://doi.org/10.1109/ACCESS.2018.2845876
  16. Nicholas A Christakis and James H Fowler. 2013. Social contagion theory: examining dynamic social networks and human behavior. Statistics in medicine 32, 4 (2013), 556ś577. https://doi.org/10.1002/sim.5408
  17. Nicholas A. Christakis, James H. Fowler, and Susan K. Walker. 2011. Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. Journal of Family Theory & Review 3, 3 (2011), 220ś224. https://doi.org/10.1111/j.1756-2589.2011.00097.x
  18. Aaron Clauset, M. E. J. Newman, and Cristopher Moore. 2004. Finding community structure in very large networks. Phys. Rev. E 70 (Dec 2004), 066111. Issue 6. https://doi.org/10.1103/PhysRevE.70.066111
  19. F David, G Ryan, and A Rossi. 2012. A Dynamical System for PageRank with Time-Dependent Teleportation. Internet Mathematics, Purdue University 10 (2012). Issue 1. https://doi.org/10.1080/15427951.2013.814092
  20. Sam De Winter, Tim Decuypere, Sandra Mitrović, Bart Baesens, and Jochen De Weerdt. 2018. Combining Temporal Aspects of Dynamic Networks with Node2Vec for a More Eicient Dynamic Link Prediction. In Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018). IEEE Press, Barcelona, Spain, 1234ś1241. https: //doi.org/10.5555/3382225.3382474
  21. E. W. Dijkstra. 1959. A note on two problems in connexion with graphs. Numer. Math. 1, 1 (01 Dec 1959), 269ś271. https://doi.org/10. 1007/BF01386390
  22. Aswathy Divakaran and Anuraj Mohan. 2020. Temporal link prediction: A survey. New Generation Computing 38, 1 (2020), 213ś258. https://doi.org/10.1007/s00354-019-00065-z
  23. Janardhan Rao Doppa, Jun Yu, Prasad Tadepalli, and Lise Getoor. 2010. Learning Algorithms for Link Prediction Based on Chance Constraints. In Proceedings of the 2010 European Conference on Machine Learning and Knowledge Discovery in Databases: Part I (Barcelona, Spain) (ECML PKDD 2010). Springer-Verlag, Berlin, Heidelberg, 344ś360. https://doi.org/10.5555/1888258.1888288
  24. Michael Fire, Lena Tenenboim, Ofrit Lesser, Rami Puzis, Lior Rokach, and Yuval Elovici. 2011. Link Prediction in Social Networks Using Computationally Eicient Topological Features. In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing. IEEE, Boston, Massachusetts, USA, 73ś80. https://doi.org/10.1109/PASSAT/ SocialCom.2011.20
  25. Robert W. Floyd. 1962. Algorithm 97: Shortest Path. Commun. ACM 5, 6 (June 1962), 345ś. https://doi.org/10.1145/367766.368168
  26. Francois Fouss, Alain Pirotte, Jean-Michel Renders, and Marco Saerens. 2007. Random-Walk Computation of Similarities Between Nodes of a Graph with Application to Collaborative Recommendation. IEEE Trans. on Knowl. and Data Eng. 19, 3 (March 2007), 355ś369. https://doi.org/10.1109/TKDE.2007.46
  27. Wenjie Fu, Le Song, and Eric P. Xing. 2009. Dynamic Mixed Membership Blockmodel for Evolving Networks. In Proceedings of the 26th Annual International Conference on Machine Learning (Montreal, Quebec, Canada) (ICML 2009). Association for Computing Machinery, New York, NY, USA, 329ś336. https://doi.org/10.1145/1553374.1553416
  28. Palash Goyal, Nitin Kamra, Xinran He, and Yan Liu. 2018. DynGEM: Deep Embedding Method for Dynamic Graphs. CoRR abs/1805.11273 (2018). https://doi.org/10.48550/arXiv.1811.10734
  29. WANG Guan-yu. 2011. Algorithm for detecting community of complex network based on clustering. Computer Engineering 10 (2011), 021.
  30. Mohammad Al Hasan and Mohammed J. Zaki. 2011. A Survey of Link Prediction in Social Networks. Springer US, Boston, MA, 243ś275. https://doi.org/10.1007/978-1-4419-8462-3_9
  31. Hsun-Hui Huang and Horng-Chang Yang. 2012. Semantic Clustering-Based Community Detection in an Evolving Social Network. In 2012 Sixth International Conference on Genetic and Evolutionary Computing. IEEE, 91ś94. https://doi.org/10.1109/ICGEC.2012.130
  32. Zan Huang, Xin Li, and Hsinchun Chen. 2005. Link Prediction Approach to Collaborative Filtering. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (Denver, CO, USA) (JCDL 2005). Association for Computing Machinery, New York, NY, USA, 141ś142. https://doi.org/10.1145/1065385.1065415
  33. Paul Jaccard. 1901. Distribution de la lore alpine dans le bassin des Dranses et dans quelques régions voisines. Bull Soc Vaudoise Sci Nat 37 (1901), 241ś272.
  34. Muhammad Aqib Javed, Muhammad Shahzad Younis, Siddique Latif, Junaid Qadir, and Adeel Baig. 2018. Community detection in networks: A multidisciplinary review. Journal of Network and Computer Applications 108 (2018), 87ś111. https://doi.org/10.1016/j.jnca. 2018.02.011
  35. Hang-Hyun Jo and Takayuki Hiraoka. 2019. Bursty Time Series Analysis for Temporal Networks. Springer International Publishing, Cham, 161ś179. https://doi.org/10.1007/978-3-030-23495-9_9
  36. Hisashi Kashima and Naoki Abe. 2006. A Parameterized Probabilistic Model of Network Evolution for Supervised Link Prediction. In Proceedings of the Sixth International Conference on Data Mining (ICDM 2006). IEEE Computer Society, Washington, DC, USA, 340ś349. https://doi.org/10.1109/ICDM.2006.8
  37. István A. Kovács, Katja Luck, Kerstin Spirohn, Yang Wang, Carl Pollis, Sadie Schlabach, Wenting Bian, Dae-Kyum Kim, Nishka Kishore, Tong Hao, Michael A. Calderwood, Marc Vidal, and Albert-Laszló Barabási. 2019. Network-based prediction of protein interactions. Nature Communications 10 (2019), 8 pages. Issue 1. https://doi.org/10.1038/s41467-019-09177-y
  38. Mark Kozdoba and Shie Mannor. 2015. Community Detection via Measure Space Embedding. In Proceedings of the 28th International Conference on Neural Information Processing Systems -Volume 2 (Montreal, Canada) (NIPS 2015, Vol. 28). MIT Press, Cambridge, MA, USA, 2890ś2898. https://doi.org/10.5555/2969442.2969563
  39. Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. 2004. Estimating mutual information. Phys. Rev. E 69 (Jun 2004), 066138. Issue 6. https://doi.org/10.1103/PhysRevE.69.066138
  40. Ajay Kumar, Shashank Sheshar Singh, Kuldeep Singh, and Bhaskar Biswas. 2020. Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and its Applications 553 (2020), 124289. https://doi.org/10.1016/j.physa.2020. 124289
  41. Mukesh Kumar, Shivansh Mishra, and Bhaskar Biswas. 2022. Features fusion based link prediction in dynamic neworks. Journal of Computational Science 57 (2022), 101493. https://doi.org/10.1016/j.jocs.2021.101493
  42. Mukesh Kumar, Shivansh Mishra, and Bhaskar Biswas. 2022. PWAF : Path Weight Aggregation Feature for link prediction in dynamic networks. Computer Communications 191 (2022), 438ś458. https://doi.org/10.1016/j.comcom.2022.05.019
  43. Mukesh Kumar, Shivansh Mishra, Rahul Deo Pandey, and Bhaskar Biswas. 2022. CFLP: A new cost based feature for link prediction in dynamic networks. Journal of Computational Science 62 (2022), 101726. https://doi.org/10.1016/j.jocs.2022.101726
  44. J Kunegis. 2017. Reality Mining network dataset ś KONECT. http://konect.cc/networks/mit
  45. Andrea Lancichinetti, Santo Fortunato, and János Kertész. 2009. Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11, 3 (mar 2009), 033015. https://doi.org/10.1088/1367-2630/11/3/033015
  46. Taisong Li, Jiawei Zhang, Philip S. Yu, Yan Zhang, and Yonghong Yan. 2018. Deep Dynamic Network Embedding for Link Prediction. IEEE Access 6 (2018), 29219ś29230. https://doi.org/10.1109/ACCESS.2018.2839770
  47. David Liben-Nowell and Jon Kleinberg. 2003. The Link Prediction Problem for Social Networks. In Proceedings of the Twelfth International Conference on Information and Knowledge Management (New Orleans, LA, USA) (CIKM 2003). ACM, New York, NY, USA, 556ś559. https://doi.org/10.1145/956863.956972
  48. Weiping Liu and Linyuan Lü. 2010. Link prediction based on local random walk. EPL (Europhysics Letters) 89, 5 (2010), 58007. https://doi.org/10.1209/0295-5075/89/58007
  49. Zhen Liu, Qian-Ming Zhang, Linyuan Lü, and Tao Zhou. 2011. Link prediction in complex networks: A local naïve Bayes model. EPL (Europhysics Letters) 96, 4 (nov 2011), 48007. https://doi.org/10.1209/0295-5075/96/48007
  50. Linyuan Lü, Ci-Hang Jin, and Tao Zhou. 2009. Similarity index based on local paths for link prediction of complex networks. Phys. Rev. E 80 (2009), 046122. Issue 4. https://doi.org/10.1103/PhysRevE.80.046122
  51. Xiaoke Ma, Penggang Sun, and Guimin Qin. 2017. Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability. Pattern Recognition 71 (2017), 361ś374. https://doi.org/10.1016/j.patcog.2017.06.025
  52. Radosław Michalski, Sebastian Palus, and Przemysław Kazienko. 2011. Matching Organizational Structure and Social Network Extracted from Email Communication. In Business Information Systems, Witold Abramowicz (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 197ś206. https://doi.org/10.1007/978-3-642-21863-7_17
  53. Sneha Mishra, Shashank Sheshar Singh, Shivansh Mishra, and Bhaskar Biswas. 2021. TCD2: Tree-based community detection in dynamic social networks. Expert Systems with Applications 169 (2021), 114493. https://doi.org/10.1016/j.eswa.2020.114493
  54. Ruchi Mittal and MPS Bhatia. 2021. Classiication and Comparative Evaluation of Community Detection Algorithms. Archives of Computational Methods in Engineering 28, 3 (2021), 1417ś1428. https://doi.org/10.1007/s11831-020-09421-5
  55. M. E. J. Newman. 2001. Clustering and preferential attachment in growing networks. Phys. Rev. E 64 (2001), 025102. Issue 2. https://doi.org/10.1103/PhysRevE.64.025102
  56. M. E. J. Newman. 2006. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74 (Sep 2006), 036104. Issue 3. https://doi.org/10.1103/PhysRevE.74.036104
  57. M. E. J. Newman. 2006. Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103, 23 (2006), 8577ś8582. https://doi.org/10.1073/pnas.0601602103
  58. M.E. J. Newman. 2005. A measure of betweenness centrality based on random walks. Social Networks 27, 1 (2005), 39ś54. https: //doi.org/10.1016/j.socnet.2004.11.009
  59. Alper Ozcan and Sule Gunduz Oguducu. 2017. Supervised temporal link prediction using time series of similarity measures. In 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, Milan, Italy, 519ś521. https://doi.org/10.1109/ICUFN. 2017.7993838
  60. Alper Ozcan and Sule Gunduz Oguducu. 2018. Link prediction in evolving heterogeneous networks using the NARX neural networks. Knowledge and Information Systems 55, 2 (2018), 333ś360. https://doi.org/10.1007/s10115-017-1073-x
  61. Alper Ozcan and Sule Gunduz Oguducu. 2019. Multivariate time series link prediction for evolving heterogeneous network. International Journal of Information Technology & Decision Making 18, 01 (2019), 241ś286. https://doi.org/10.1142/S0219622018500530
  62. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 85 (2011), 2825ś2830. http://jmlr.org/papers/v12/pedregosa11a.html
  63. Tiago P. Peixoto. 2014. Hierarchical Block Structures and High-Resolution Model Selection in Large Networks. Phys. Rev. X 4 (Mar 2014), 011047. Issue 1. https://doi.org/10.1103/PhysRevX.4.011047
  64. Usha Nandini Raghavan, Réka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical review. E, Statistical, nonlinear, and soft matter physics 76, 3 Pt 2 (September 2007), 036106. https: //doi.org/10.1103/physreve.76.036106
  65. Brian C. Ross. 2014. Mutual Information between Discrete and Continuous Data Sets. PLOS ONE 9, 2 (02 2014), 1ś5. https: //doi.org/10.1371/journal.pone.0087357
  66. Giulio Rossetti, Letizia Milli, and Rémy Cazabet. 2019. CDLIB: a python library to extract, compare and evaluate communities from complex networks. Applied Network Science 4 (2019). https://doi.org/10.1007/s41109-019-0165-9 https://cdlib.readthedocs.io/en/, https://github.com/GiulioRossetti/cdlib.
  67. Ryan A. Rossi and Nesreen K. Ahmed. 2015. The Network Data Repository with Interactive Graph Analytics and Visualization. http://networkrepository.com
  68. Purnamrita Sarkar, Deepayan Chakrabarti, and Michael I. Jordan. 2012. Nonparametric Link Prediction in Dynamic Networks. In Proceedings of the 29th International Coference on International Conference on Machine Learning (Edinburgh, Scotland) (ICML 2012). Omnipress, Madison, WI, USA, 1897ś1904. https://doi.org/10.5555/3042573.3042815
  69. Shashank Sheshar Singh, Ajay Kumar, Kuldeep Singh, and Bhaskar Biswas. 2019. C2IM: Community based context-aware inluence maximization in social networks. Physica A: Statistical Mechanics and its Applications 514 (2019), 796ś818. https://doi.org/10.1016/j. physa.2018.09.142
  70. Shashank Sheshar Singh, Shivansh Mishra, Ajay Kumar, and Bhaskar Biswas. 2020. CLP-ID: Community-based link prediction using information difusion. Information Sciences 514 (2020), 402ś433. https://doi.org/10.1016/j.ins.2019.11.026
  71. Shashank Sheshar Singh, Divya Srivastva, Madhushi Verma, and Jagendra Singh. 2021. Inluence maximization frameworks, performance, challenges and directions on social network: A theoretical study. Journal of King Saud University -Computer and Information Sciences (2021). https://doi.org/10.1016/j.jksuci.2021.08.009
  72. Heung-Il Suk. 2017. Chapter 1 -An Introduction to Neural Networks and Deep Learning. In Deep Learning for Medical Image Analysis, S. Kevin Zhou, Hayit Greenspan, and Dinggang Shen (Eds.). Academic Press, 3ś24. https://doi.org/10.1016/B978-0-12-810408-8.00002-X
  73. Bishenghui Tao, Hong-Ning Dai, Jiajing Wu, Ivan Wang-Hei Ho, Zibin Zheng, and Chak Fong Cheang. 2022. Complex Network Analysis of the Bitcoin Transaction Network. IEEE Transactions on Circuits and Systems II: Express Briefs 69, 3 (2022), 1009ś1013. https://doi.org/10.1109/TCSII.2021.3127952
  74. V. A. Traag, R. Aldecoa, and J.-C. Delvenne. 2015. Detecting communities using asymptotical surprise. Phys. Rev. E 92 (Aug 2015), 022816. Issue 2. https://doi.org/10.1103/PhysRevE.92.022816
  75. V. A. Traag, G. Krings, and P. Van Dooren. 2013. Signiicant Scales in Community Structure. Scientiic Reports 3 (October 2013), 10 pages. Issue 1. https://doi.org/10.1038/srep02930
  76. V. A. Traag, P. Van Dooren, and Y. Nesterov. 2011. Narrow scope for resolution-limit-free community detection. Phys. Rev. E 84 (Jul 2011), 016114. Issue 1. https://doi.org/10.1103/PhysRevE.84.016114
  77. V. A. Traag, L. Waltman, and N. J. van Eck. 2019. From Louvain to Leiden: guaranteeing well-connected communities. Scientiic Reports 9 (March 2019), 12 pages. Issue 1. https://doi.org/10.1038/s41598-019-41695-z
  78. Friedhelm Victor and Andrea Marie Weintraud. 2021. Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges. In Proceedings of the Web Conference 2021. Association for Computing Machinery, New York, NY, USA, 23ś32. https: //doi.org/10.1145/3442381.3449824
  79. Dashun Wang, Dino Pedreschi, Chaoming Song, Fosca Giannotti, and Albert-Laszlo Barabasi. 2011. Human Mobility, Social Ties, and Link Prediction. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Diego, California, USA) (KDD 2011). Association for Computing Machinery, New York, NY, USA, 1100ś1108. https://doi.org/10.1145/2020408. 2020581
  80. Mang-Hui Wang and CP Hung. 2003. Extension neural network and its applications. Neural Networks 16, 5-6 (2003), 779ś784. https://doi.org/10.1016/S0893-6080(03)00104-7
  81. Fang Wei, Weining Qian, Chen Wang, and Aoying Zhou. 2009. Detecting overlapping community structures in networks. World Wide Web 12, 2 (2009), 235ś261. https://doi.org/10.1007/s11280-009-0060-x
  82. Tsunghan Wu, Cheng-Shang Chang, and Wanjiun Liao. 2018. Tracking network evolution and their applications in structural network analysis. IEEE Transactions on Network Science and Engineering 6, 3 (2018), 562ś575. https://doi.org/10.1109/TNSE.2018.2815686
  83. Zhihao Wu, Youfang Lin, Huaiyu Wan, and Waleed Jamil. 2016. Predicting top-L missing links with node and link clustering information in large-scale networks. Journal of Statistical Mechanics: Theory and Experiment 2016, 8 (2016), 083202. https://doi.org/10.1088/1742- 5468/2016/08/083202
  84. Zhihao Wu, Youfang Lin, Jing Wang, and Steve Gregory. 2016. Link prediction with node clustering coeicient. Physica A: Statistical Mechanics and its Applications 452 (2016), 1ś8. https://doi.org/10.1016/j.physa.2016.01.038
  85. Jierui Xie and Boleslaw K. Szymanski. 2011. Community Detection Using a Neighborhood Strength Driven Label Propagation Algorithm. In Proceedings of the 2011 IEEE Network Science Workshop (NSW 2011). IEEE Computer Society, USA, 188ś195. https: //doi.org/10.1109/NSW.2011.6004645
  86. Guangxu Xun, Yujiu Yang, Liangwei Wang, and Wenhuang Liu. 2012. Latent community discovery with network regularization for core actors clustering. In Proceedings of COLING 2012: Posters. The COLING 2012 Organizing Committee, Mumbai, India, 1351ś1360. https://aclanthology.org/C12-2132
  87. Yasser Yasami and Farshad Safaei. 2018. A novel multilayer model for missing link prediction and future link forecasting in dynamic complex networks. Physica A: Statistical Mechanics and its Applications 492 (2018), 2166ś2197. https://doi.org/10.1016/j.physa.2017.11.134
  88. Jieping Ye. 2007. Least Squares Linear Discriminant Analysis. In Proceedings of the 24th International Conference on Machine Learning (Corvalis, Oregon, USA) (ICML 2007). Association for Computing Machinery, New York, NY, USA, 1087ś1093. https://doi.org/10.1145/ 1273496.1273633
  89. Linbin Yu and Chris Ding. 2010. Network Community Discovery: Solving Modularity Clustering via Normalized Cut. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs (Washington, D.C.) (MLG 2010). Association for Computing Machinery, New York, NY, USA, 34ś36. https://doi.org/10.1145/1830252.1830257
  90. Shihua Zhang, X-M Ning, and X-S Zhang. 2007. Graph kernels, hierarchical clustering, and network community structure: experiments and comparative analysis. The European Physical Journal B 57, 1 (2007), 67ś74. https://doi.org/10.1140/epjb/e2007-00146-y
  91. Shao-Zhong Zhang, Zhao-Xi Fang, Jun-Gan Chen, and Jiong Shi. 2013. Community clustering model for E-commerce trust based on social network. Journal of ZheJiang University (Engineering Science) 47, 4 (2013), 656ś661.
  92. Lin Zhao, Sourav Sen Gupta, Arijit Khan, and Robby Luo. 2021. Temporal Analysis of the Entire Ethereum Blockchain Network. In Proceedings of the Web Conference 2021. Association for Computing Machinery, New York, NY, USA, 2258ś2269. https://doi.org/10.1145/ 3442381.3449916
  93. Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. 2018. Dynamic network embedding by modeling triadic closure process. In Proceedings of the Thirty-Second AAAI Conference on Artiicial Intelligence and Thirtieth Innovative Applications of Artiicial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artiicial Intelligence (New Orleans, Louisiana, USA) (AAAI 2018/IAAI 2018/EAAI 2018, Vol. 32). AAAI Press, Article 71, 8 pages. https://doi.org/10.5555/3504035.3504106
  94. Jianhan Zhu, Jun Hong, and John G. Hughes. 2002. Using Markov Models for Web Site Link Prediction. In Proceedings of the Thirteenth ACM Conference on Hypertext and Hypermedia (College Park, Maryland, USA) (HYPERTEXT 2002). Association for Computing Machinery, New York, NY, USA, 169ś170. https://doi.org/10.1145/513338.513381
  95. Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, and Aram Galstyan. 2016. Scalable temporal latent space inference for link prediction in dynamic social networks. IEEE Transactions on Knowledge and Data Engineering 28, 10 (Oct. 2016), 2765ś2777. https://doi.org/10.1109/TKDE.2016.2591009 Publisher Copyright: © 1989-2012 IEEE..
  96. Yong Zou, Reik V. Donner, Norbert Marwan, Jonathan F. Donges, and Jürgen Kurths. 2019. Complex network approaches to nonlinear time series analysis. Physics Reports 787 (2019), 1ś97. https://doi.org/10.1016/j.physrep.2018.10.005

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