Temporal Link Prediction: A Survey

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. 1.

    Abdi, H.: The eigen-decomposition: Eigenvalues and eigenvectors. In: Encyclopedia of Measurement and Statistics, pp. 304–308 (2007)

  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)

    MathSciNet  MATH  Google 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)

    MathSciNet  MATH  Google 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)

  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)

  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)

  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)

    MathSciNet  Google 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)

    MATH  Google Scholar 

  13. 13.

    Brockwell, P.J., Davis, R.A., Calder, M.V.: Introduction to Time Series and Forecasting, vol. 2. Springer, Cham (2002)

    MATH  Google 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)

    MathSciNet  Google 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)

  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)

  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)

  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)

  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)

  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)

    MathSciNet  Google 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)

  28. 28.

    Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: IJCAI, vol. 99, pp. 1300–1309 (1999)

  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)

  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)

  31. 31.

    Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. In: Linear Algebra, pp. 134–151. Springer, Berlin, Heidelberg (1971)

  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)

    MathSciNet  MATH  Google 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)

  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)

  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)

  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)

  43. 43.

    Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    MATH  Google 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)

  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)

    MathSciNet  Google 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)

  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)

  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)

  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)

  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)

  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)

    MathSciNet  MATH  Google 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)

    MathSciNet  MATH  Google 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)

  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)

  70. 70.

    Ouzienko, V., Guo, Y., Obradovic, Z.: Prediction of attributes and links in temporal social networks. In: ECAI, pp. 1121–1122 (2010)

  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)

  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)

  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)

  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)

  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)

    MathSciNet  Google 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)

  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)

  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)

  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)

    MathSciNet  MATH  Google 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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

    MathSciNet  Google 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)

  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)

  108. 108.

    Yu, X., Chu, T.: Dynamic link prediction using restricted Boltzmann machine. In: Chinese Automation Congress (CAC), pp. 4089–4092. IEEE (2017)

  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)

  112. 112.

    Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009)

    MATH  Google 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 

Download references

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Aswathy Divakaran.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Verify currency and authenticity via CrossMark

Cite this article

Divakaran, A., Mohan, A. Temporal Link Prediction: A Survey. New Gener. Comput. 38, 213–258 (2020). https://doi.org/10.1007/s00354-019-00065-z

Download citation

Keywords

  • Dynamic networks
  • Temporal networks
  • Link prediction