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Temporal Link Prediction: A Survey

  • Aswathy DivakaranEmail author
  • Anuraj Mohan
Article
  • 11 Downloads

Abstract

The evolutionary behavior of temporal networks has gained the attention of researchers with its ubiquitous applications in a variety of real-world scenarios. Learning evolutionary behavior of networks is directly related to link prediction problem, as the addition or removal of new links or edges over time leads to the network evolution. With the rise of large-scale temporal networks such as social networks, temporal link prediction has become an interesting field of study. In this work, we provide a detailed survey of various researches carried out in the direction of temporal link prediction. We build a taxonomy of temporal link prediction methods based on various approaches used and discuss the works which come under each category. Further, we present the challenges and directions for future works.

Keywords

Dynamic networks Temporal networks Link prediction 

Notes

Acknowledgements

The infrastructure used for conducting this study is funded by FIST which is sanctioned by DST to NSS College of Engineering, Palakkad. We would like to express our gratitude to the Department of Computer Science and Engineering, NSS College of Engineering, Palakkad, for providing the required infrastructure.

References

  1. 1.
    Abdi, H.: The eigen-decomposition: Eigenvalues and eigenvectors. In: Encyclopedia of Measurement and Statistics, pp. 304–308 (2007)Google Scholar
  2. 2.
    Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)Google Scholar
  3. 3.
    Ahmed, N.M., Chen, L.: New approaches for link prediction in temporal social networks. Comput. Model. New Technol. 18, 87–94 (2014)Google Scholar
  4. 4.
    Ahmed, N.M., Chen, L.: An efficient algorithm for link prediction in temporal uncertain social networks. Inf. Sci. 331, 120–136 (2016)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Ahmed, N.M., Chen, L., Wang, Y., Li, B., Li, Y., Liu, W.: Sampling-based algorithm for link prediction in temporal networks. Inf. Sci. 374, 1–14 (2016)MathSciNetGoogle Scholar
  6. 6.
    Ahmed, N.M., Chen, L., Wang, Y., Li, B., Li, Y., Liu, W.: Deepeye: link prediction in dynamic networks based on non-negative matrix factorization. Big Data Min. Anal. 1(1), 19–33 (2018)Google Scholar
  7. 7.
    Aiello, L.M., Barrat, A., Schifanella, R., Cattuto, C., Markines, B., Menczer, F.: Friendship prediction and homophily in social media. ACM Trans. Web (TWEB) 6(2), 9 (2012)Google Scholar
  8. 8.
    Al Hasan, M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: SDM06: Workshop on Link Analysis, Counter-terrorism and Security (2006)Google Scholar
  9. 9.
    Al Hasan, M., Zaki, M.J.: A survey of link prediction in social networks. In: Social Network Data Analytics, pp. 243–275. Springer, Boston, MA (2011)Google Scholar
  10. 10.
    Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pp. 635–644. ACM (2011)Google Scholar
  11. 11.
    Bliss, C.A., Frank, M.R., Danforth, C.M., Dodds, P.S.: An evolutionary algorithm approach to link prediction in dynamic social networks. J. Comput. Sci. 5(5), 750–764 (2014)MathSciNetGoogle Scholar
  12. 12.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)Google Scholar
  13. 13.
    Brockwell, P.J., Davis, R.A., Calder, M.V.: Introduction to Time Series and Forecasting, vol. 2. Springer, Cham (2002)zbMATHGoogle Scholar
  14. 14.
    Bütün, E., Kaya, M., Alhajj, R.: Extension of neighbor-based link prediction methods for directed, weighted and temporal social networks. Inf. Sci. 463, 152–165 (2018)MathSciNetGoogle Scholar
  15. 15.
    Casteigts, A., Flocchini, P., Quattrociocchi, W., Santoro, N.: Time-varying graphs and dynamic networks. Int. J. Parallel Emerg. Distrib. Syst. 27(5), 387–408 (2012)Google Scholar
  16. 16.
    Chen, H.H., Gou, L., Zhang, X.L., Giles, C.L.: Discovering missing links in networks using vertex similarity measures. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 138–143 (2012)Google Scholar
  17. 17.
    Chib, S., Greenberg, E.: Understanding the metropolis-hastings algorithm. Am. Statist. 49(4), 327–335 (1995)Google Scholar
  18. 18.
    Chiu, C., Zhan, J.: Deep learning for link prediction in dynamic networks using weak estimators. In: IEEE Access, pp. 35937–35945 (2018)Google Scholar
  19. 19.
    Cholette, P.A.: Prior information and ARIMA forecasting. J. Forecast. 1(4), 375–383 (1982)Google Scholar
  20. 20.
    Choudhury, N., Uddin, S.: Evolutionary community mining for link prediction in dynamic networks. In: International Conference on Complex Networks and their Applications, pp. 127–138. Springer (2017)Google Scholar
  21. 21.
    Clauset, A., Moore, C., Newman, M.E.: Hierarchical structure and the prediction of missing links in networks. Nature 453(7191), 98 (2008)Google Scholar
  22. 22.
    Das, S., Das, S.K.: A probabilistic link prediction model in time-varying social networks. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2017)Google Scholar
  23. 23.
    Dong, L., Li, Y., Yin, H., Le, H., Rui, M.: The algorithm of link prediction on social network. Math. Probl. Eng. 2013 (2013)Google Scholar
  24. 24.
    Dunlavy, D.M., Kolda, T.G., Acar, E.: Temporal link prediction using matrix and tensor factorizations. ACM Trans. Knowl. Discov. Data (TKDD) 5(2), 10 (2011)Google Scholar
  25. 25.
    Estrada, E., Hatano, N.: Communicability in complex networks. Phys. Rev. E 77(3), 036111 (2008)MathSciNetGoogle Scholar
  26. 26.
    Faber, N.K.M., Bro, R., Hopke, P.K.: Recent developments in CANDECOMP/PARAFAC algorithms: a critical review. Chemom. Intell. Lab. Syst. 65(1), 119–137 (2003)Google Scholar
  27. 27.
    Fang, C., Kohram, M., Meng, X., Ralescu, A.: Graph embedding framework for link prediction and vertex behavior modeling in temporal social networks. In: Proceedings of the SIGKDD Workshop on Social Network Mining and Analysis (2011)Google Scholar
  28. 28.
    Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: IJCAI, vol. 99, pp. 1300–1309 (1999)Google Scholar
  29. 29.
    Gael, J.V., Teh, Y.W., Ghahramani, Z.: The infinite factorial hidden Markov model. In: Advances in Neural Information Processing Systems, pp. 1697–1704 (2009)Google Scholar
  30. 30.
    Gao, S., Denoyer, L., Gallinari, P.: Temporal link prediction by integrating content and structure information. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1169–1174. ACM (2011)Google Scholar
  31. 31.
    Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. In: Linear Algebra, pp. 134–151. Springer, Berlin, Heidelberg (1971)Google Scholar
  32. 32.
    Goyal, P., Kamra, N., He, X., Liu, Y.: Dyngem: deep embedding method for dynamic graphs (2018). arXiv:1805.11273
  33. 33.
    Guimerà, R., Sales-Pardo, M.: Missing and spurious interactions and the reconstruction of complex networks. Proc. Natl. Acad. Sci. 106(52), 22073–22078 (2009)Google Scholar
  34. 34.
    Güneş, İ., Gündüz-Öğüdücü, Ş., Çataltepe, Z.: Link prediction using time series of neighborhood-based node similarity scores. Data Min. Knowl. Discov. 30(1), 147–180 (2016)MathSciNetzbMATHGoogle Scholar
  35. 35.
    Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolut. Comput. 9(2), 159–195 (2001)Google Scholar
  36. 36.
    Heckerman, D., Meek, C., Koller, D.: Probabilistic entity-relationship models, PRMs, and plate models. In: Introduction to Statistical Relational Learning, pp. 201–238 (2007)Google Scholar
  37. 37.
    Hisano, R.: Semi-supervised graph embedding approach to dynamic link prediction. In: International Workshop on Complex Networks, pp. 109–121. Springer, Cham (2018)Google Scholar
  38. 38.
    Holme, P., Saramäki, J.: Temporal networks. Phys. Rep. 519(3), 97–125 (2012)Google Scholar
  39. 39.
    Ibrahim, N.M.A., Chen, L.: Link prediction in dynamic social networks by integrating different types of information. Appl. Intell. 42(4), 738–750 (2015)Google Scholar
  40. 40.
    Jaccard, P.: Étude comparative de la distribution florale dans une portion des Alpes et des jura. Bull. Soc. Vaudoise Sci. Nat. 37, 547–579 (1901)Google Scholar
  41. 41.
    Juszczyszyn, K., Musial, K., Budka, M.: Link prediction based on subgraph evolution in dynamic social networks. In: 3rd IEEE International Conference on Privacy, Security, Risk and Trust and Third IEEE International Conference on Social Computing, pp. 27–34 (2011)Google Scholar
  42. 42.
    Kashima, H., Abe, N.: A parameterized probabilistic model of network evolution for supervised link prediction. In: 6th International Conference on Data Mining (ICDM’06), pp. 340–349. IEEE (2006)Google Scholar
  43. 43.
    Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)zbMATHGoogle Scholar
  44. 44.
    Kim, M., Leskovec, J.: The network completion problem: inferring missing nodes and edges in networks. In: Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 47–58. Society for Industrial and Applied Mathematics (2011)Google Scholar
  45. 45.
    Kossinets, G.: Effects of missing data in social networks. Soc. Netw. 28(3), 247–268 (2006)Google Scholar
  46. 46.
    Kostakos, V.: Temporal graphs. Phys. A Stat. Mech. Appl. 388(6), 1007–1023 (2009)MathSciNetGoogle Scholar
  47. 47.
    Kunegis, J., Lommatzsch, A.: Learning spectral graph transformations for link prediction. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 561–568. ACM (2009)Google Scholar
  48. 48.
    Lakshmi, T.J., Bhavani, S.D.: Temporal probabilistic measure for link prediction in collaborative networks. Appl. Intell. 47(1), 83–95 (2017)Google Scholar
  49. 49.
    Lei, K., Qin, M., Bai, B., Zhang, G.: Adaptive multiple non-negative matrix factorization for temporal link prediction in dynamic networks. In: Proceedings of the 2018 Workshop on Network Meets AI & ML, pp. 28–34. ACM (2018)Google Scholar
  50. 50.
    Li, J., Cheng, K., Wu, L., Liu, H.: Streaming link prediction on dynamic attributed networks. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 369–377. ACM (2018)Google Scholar
  51. 51.
    Li, T., Zhang, J., Philip, S.Y., Zhang, Y., Yan, Y.: Deep dynamic network embedding for link prediction. IEEE Access 6, 29219–29230 (2018)Google Scholar
  52. 52.
    Li, X., Du, N., Li, H., Li, K., Gao, J., Zhang, A.: A deep learning approach to link prediction in dynamic networks. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 289–297. SIAM (2014)Google Scholar
  53. 53.
    Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)Google Scholar
  54. 54.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)Google Scholar
  55. 55.
    Lichtenwalter, R.N., Chawla, N.V.: Vertex collocation profiles: subgraph counting for link analysis and prediction. In: Proceedings of the 21st International Conference on World Wide Web, pp. 1019–1028. ACM (2012)Google Scholar
  56. 56.
    Liu, W., Lü, L.: Link prediction based on local random walk. EPL (Europhysics Letters) 89(5), 58007 (2010)Google Scholar
  57. 57.
    Lü, L., Jin, C.H., Zhou, T.: Similarity index based on local paths for link prediction of complex networks. Phys. Rev. E 80(4), 046122 (2009)Google Scholar
  58. 58.
    Lü, L., Medo, M., Yeung, C.H., Zhang, Y.C., Zhang, Z.K., Zhou, T.: Recommender systems. Phys. Rep. 519(1), 1–49 (2012)Google Scholar
  59. 59.
    Lü, L., Pan, L., Zhou, T., Zhang, Y.C., Stanley, H.E.: Toward link predictability of complex networks. Proc. Natl. Acad. Sci. 112(8), 2325–2330 (2015)MathSciNetzbMATHGoogle Scholar
  60. 60.
    Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A Stat. Mech. Appl. 390(6), 1150–1170 (2011)Google Scholar
  61. 61.
    Ma, X., Sun, P., Qin, G.: Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability. Pattern Recognit. 71, 361–374 (2017)Google Scholar
  62. 62.
    Ma, X., Sun, P., Wang, Y.: Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks. Phys. A Stat. Mech. Appl. 496, 121–136 (2018)Google Scholar
  63. 63.
    Meng, B., Ke, H., Yi, T.: Link prediction based on a semi-local similarity index. Chin. Phys. B 20(12), 128902 (2011)Google Scholar
  64. 64.
    Menon, A.K., Elkan, C.: Link Prediction via Matrix Factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science, vol. 6912, pp. 437–452. Springer, Berlin, Heidelberg (2011)Google Scholar
  65. 65.
    Moradabadi, B., Meybodi, M.R.: Link prediction based on temporal similarity metrics using continuous action set learning automata. Phys. A Stat. Mech. Appl. 460, 361–373 (2016)MathSciNetzbMATHGoogle Scholar
  66. 66.
    Muniz, C.P., Goldschmidt, R., Choren, R.: Combining contextual, temporal and topological information for unsupervised link prediction in social networks. Knowl. Based Syst. 156, 129–137 (2018)Google Scholar
  67. 67.
    Narasimhan, J., Holder, L.: Feature engineering for supervised link prediction on dynamic social networks. In: Proceedings of the International Conference on Data Mining (DMIN), p. 1. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2014)Google Scholar
  68. 68.
    Newman, M.E.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)Google Scholar
  69. 69.
    Nguyen, G.H., Lee, J.B., Rossi, R.A., Ahmed, N.K., Koh, E., Kim, S.: Continuous-time dynamic network embeddings. In: Companion Proceedings of the Web Conference 2018, pp. 969–976. International World Wide Web Conferences Steering Committee (2018)Google Scholar
  70. 70.
    Ouzienko, V., Guo, Y., Obradovic, Z.: Prediction of attributes and links in temporal social networks. In: ECAI, pp. 1121–1122 (2010)Google Scholar
  71. 71.
    Oyama, S., Hayashi, K., Kashima, H.: Cross-temporal link prediction. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 1188–1193. IEEE (2011)Google Scholar
  72. 72.
    Özcan, A., Öğüdücü, Ş.G.: Multivariate temporal link prediction in evolving social networks. In: IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), pp. 185–190. IEEE (2015)Google Scholar
  73. 73.
    Özcan, A., Öğüdücü, Ş.G.: Temporal link prediction using time series of quasi-local node similarity measures. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 381–386. IEEE (2016)Google Scholar
  74. 74.
    Özcan, A., Öğüdücü, Ş.G.: Supervised temporal link prediction using time series of similarity measures. In: 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 519–521. IEEE (2017)Google Scholar
  75. 75.
    Pavlov, M., Ichise, R.: Finding experts by link prediction in co-authorship networks. FEWS 290, 42–55 (2007)Google Scholar
  76. 76.
    Pech, R., Hao, D., Lee, Y.L., Yuan, Y., Zhou, T.: Link prediction via linear optimization. Phys. A Stat. Mech. Appl. 528, 121319 (2019)MathSciNetGoogle Scholar
  77. 77.
    Pech, R., Hao, D., Pan, L., Cheng, H., Zhou, T.: Link prediction via matrix completion. EPL (Europhysics Letters) 117(3), 38002 (2017)Google Scholar
  78. 78.
    Popescul, A., Ungar, L.H.: Statistical relational learning for link prediction. In: IJCAI Workshop on Learning Statistical Models from Relational Data, vol. 2003. Citeseer (2003)Google Scholar
  79. 79.
    Rahman, M., Hasan, M.A.: Link prediction in dynamic networks using graphlet. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2016. Lecture Notes in Computer Science, vol. 9851, pp. 394–409. Springer, Cham (2016)Google Scholar
  80. 80.
    Rahman, M., Saha, T.K., Hasan, M.A., Xu, K.S., Reddy, C.K.: Dylink2vec: effective feature representation for link prediction in dynamic networks (2018). arXiv:1804.05755
  81. 81.
    Ralescu, A., Kohram, M., et al.: Spectral regression with low-rank approximation for dynamic graph link prediction. IEEE Intell. Syst. 26(4), 48–53 (2011)Google Scholar
  82. 82.
    Raymond, R., Kashima, H.: Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science, vol. 6323, pp. 131–147. Springer, Berlin, Heidelberg (2010)Google Scholar
  83. 83.
    Rossetti, G., Guidotti, R., Pennacchioli, D., Pedreschi, D., Giannotti, F.: Interaction prediction in dynamic networks exploiting community discovery. In: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 553–558. IEEE (2015)Google Scholar
  84. 84.
    Sajadmanesh, S., Zhang, J., Rabiee, H.R.: NPGLM: a non-parametric method for temporal link prediction (2017). arXiv:1706.06783
  85. 85.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill (1986)Google Scholar
  86. 86.
    Sarkar, P., Chakrabarti, D., Jordan, M.: Nonparametric link prediction in dynamic networks (2012). arXiv:1206.6394
  87. 87.
    Sarkar, P., Chakrabarti, D., Jordan, M.: Nonparametric link prediction in large scale dynamic networks. Electron. J. Stat. 8(2), 2022–2065 (2014)MathSciNetzbMATHGoogle Scholar
  88. 88.
    Soares, P.R., Prudêncio, R.B.: Proximity measures for link prediction based on temporal events. Expert Syst. Appl. 40(16), 6652–6660 (2013)Google Scholar
  89. 89.
    Symeonidis, P., Mantas, N.: Spectral clustering for link prediction in social networks with positive and negative links. Soc. Netw. Anal. Min. 3(4), 1433–1447 (2013)Google Scholar
  90. 90.
    Tang, J., Wu, S., Sun, J., Su, H.: Cross-domain collaboration recommendation. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1285–1293. ACM (2012)Google Scholar
  91. 91.
    Tarrés-Deulofeu, M., Godoy-Lorite, A., Guimerà, R., Sales-Pardo, M.: Tensorial and bipartite block models for link prediction in layered networks and temporal networks. Phys. Rev. E 99(3), 032307 (2019)Google Scholar
  92. 92.
    Valverde-Rebaza, J., de Andrade Lopes, A.: Exploiting behaviors of communities of twitter users for link prediction. Soc. Netw. Anal. Min. 3(4), 1063–1074 (2013)Google Scholar
  93. 93.
    Wang, C., Mahadevan, S.: Manifold alignment using procrustes analysis. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1120–1127. ACM (2008)Google Scholar
  94. 94.
    Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: 7th IEEE International Conference on Data Mining (ICDM), pp. 322–331. IEEE (2007)Google Scholar
  95. 95.
    Wang, P., Xu, B., Wu, Y., Zhou, X.: Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58(1), 1–38 (2015)Google Scholar
  96. 96.
    Wang, T., He, X.S., Zhou, M.Y., Fu, Z.Q.: Link prediction in evolving networks based on popularity of nodes. Sci. Rep. 7(1), 7147 (2017)Google Scholar
  97. 97.
    Wang, W.Q., Zhang, Q.M., Zhou, T.: Evaluating network models: a likelihood analysis. EPL (Europhysics Letters) 98(2), 28004 (2012)Google Scholar
  98. 98.
    Wohlfarth, T., Ichise, R.: Semantic and Event-Based Approach for Link Prediction. In: Yamaguchi, T. (ed.) Practical Aspects of Knowledge Management. PAKM 2008. Lecture Notes in Computer Science, vol. 5345, pp. 50–61. Springer, Berlin, Heidelberg (2008)Google Scholar
  99. 99.
    Wu, T., Chang, C.S., Liao, W.: Tracking network evolution and their applications in structural network analysis. IEEE Trans. Knowl. Data Eng (2018)Google Scholar
  100. 100.
    Xie, H., Tang, H., Liao, Y.H.: Time series prediction based on NARX neural networks: an advanced approach. In: 2009 International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1275–1279. IEEE (2009)Google Scholar
  101. 101.
    Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.: Network representation learning with rich text information. In: 24th International Joint Conference on Artificial Intelligence, pp. 2111–2117 (2015)Google Scholar
  102. 102.
    Yang, X., Tian, Z., Cui, H., Zhang, Z.: Link prediction on evolving network using tensor-based node similarity. In: 2012 IEEE 2nd International Conference on Cloud Computing and Intelligent Systems (CCIS), vol. 1, pp. 154–158. IEEE (2012)Google Scholar
  103. 103.
    Yao, L., Wang, L., Pan, L., Yao, K.: Link prediction based on common-neighbors for dynamic social network. Proc. Comput. Sci. 83, 82–89 (2016)Google Scholar
  104. 104.
    Yasami, Y., Safaei, F.: A novel multilayer model for missing link prediction and future link forecasting in dynamic complex networks. Phys. A Stat. Mech. Appl. 492, 2166–2197 (2018)MathSciNetGoogle Scholar
  105. 105.
    Young, F.W., Hamer, R.M.: Theory and Applications of Multidimensional Scaling. Eribaum Associates, Hillsdale (1994)Google Scholar
  106. 106.
    Yu, K., Chu, W., Yu, S., Tresp, V., Xu, Z.: Stochastic relational models for discriminative link prediction. In: Advances in Neural Information Processing Systems, pp. 1553–1560 (2007)Google Scholar
  107. 107.
    Yu, W., Cheng, W., Aggarwal, C.C., Chen, H., Wang, W.: Link prediction with spatial and temporal consistency in dynamic networks. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 3343–3349 (2017)Google Scholar
  108. 108.
    Yu, X., Chu, T.: Dynamic link prediction using restricted Boltzmann machine. In: Chinese Automation Congress (CAC), pp. 4089–4092. IEEE (2017)Google Scholar
  109. 109.
    Zhang, Q.M., Xu, X.K., Zhu, Y.X., Zhou, T.: Measuring multiple evolution mechanisms of complex networks. Sci. Rep. 5, 10350 (2015)Google Scholar
  110. 110.
    Zhang, Z., Wen, J., Sun, L., Deng, Q., Su, S., Yao, P.: Efficient incremental dynamic link prediction algorithms in social network. Knowl. Based Syst. 132, 226–235 (2017)Google Scholar
  111. 111.
    Zhou, L., Yang, Y., Ren, X., Wu, F., Zhuang, Y.: Dynamic network embedding by modeling triadic closure process. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)Google Scholar
  112. 112.
    Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009)zbMATHGoogle Scholar
  113. 113.
    Zhu, J., Hong, J., Hughes, J.G.: Using markov chains for link prediction in adaptive web sites. In: Bustard, D., Liu, W., Sterritt, R. (eds.) Soft-Ware 2002: Computing in an Imperfect World. Lecture Notes in Computer Science, vol. 2311, pp. 60–73. Springer, Berlin, Heidelberg (2002)Google Scholar
  114. 114.
    Zhu, L., Guo, D., Yin, J., Ver Steeg, G., Galstyan, A.: Scalable temporal latent space inference for link prediction in dynamic social networks. IEEE Trans. Knowl. Data Eng. 28(10), 2765–2777 (2016)Google Scholar
  115. 115.
    Zhu, Y.X., Lü, L., Zhang, Q.M., Zhou, T.: Uncovering missing links with cold ends. Phys. A Stat. Mech. Appl. 391(22), 5769–5778 (2012)Google Scholar

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© Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNSS College of EngineeringPalakkadIndia

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