Joint Node-Edge Network Embedding for Link Prediction

  • Ilya Makarov
  • Olga Gerasimova
  • Pavel Sulimov
  • Ksenia Korovina
  • Leonid E. Zhukov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11179)


In this paper, we consider new formulation of graph embedding algorithm, while learning node and edge representation under common constraints. We evaluate our approach on link prediction problem for co-authorship network of HSE researchers’ publications. We compare it with existing structural network embeddings and feature-engineering models.


Graph embedding Link prediction Node2vec Machine learning 


  1. 1.
    Li, X., Chen, H.: Recommendation as link prediction: a graph kernel-based machine learning approach. In: Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries. JCDL 2009, pp. 213–216. ACM, New York (2009)Google Scholar
  2. 2.
    Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. WSDM 2011, pp. 635–644. ACM, New York (2011)Google Scholar
  3. 3.
    Adafre, S.F., de Rijke, M.: Discovering missing links in wikipedia. In: Proceedings of the 3rd International Workshop on Link Discovery. LinkKDD 2005, pp. 90–97. ACM, New York (2005)Google Scholar
  4. 4.
    Zhu, J., Hong, J., Hughes, J.G.: Using markov models for web site link prediction. In: Proceedings of the Thirteenth ACM Conference on Hypertext and Hypermedia. HYPERTEXT 2002, pp. 169–170. ACM, New York (2002)Google Scholar
  5. 5.
    Fiore, A.T., Donath, J.S.: Homophily in online dating: when do you like someone like yourself? In: CHI 2005 Extended Abstracts on Human Factors in Computing Systems. CHI EA 2005, pp. 1371–1374. ACM, New York (2005)Google Scholar
  6. 6.
    Vazquez, A., Flammini, A., Maritan, A., Vespignani, A.: Global protein function prediction from protein-protein interaction networks. Nature Biotechnol. 21(6), 697–700 (2003)CrossRefGoogle Scholar
  7. 7.
    Freschi, V.: A graph-based semi-supervised algorithm for protein function prediction from interaction maps. In: Stützle, T. (ed.) LION 2009. LNCS, vol. 5851, pp. 249–258. Springer, Heidelberg (2009). Scholar
  8. 8.
    Malin, B., Airoldi, E., Carley, K.M.: A network analysis model for disambiguation of names in lists. Comput. Math. Organ. Theory 11(2), 119–139 (2005)CrossRefGoogle Scholar
  9. 9.
    Elmagarmid, A.K., Ipeirotis, P.G., Verykios, V.S.: Duplicate record detection: a survey. IEEE Trans. Knowl. Data Eng. 19(1), 1–16 (2007)CrossRefGoogle Scholar
  10. 10.
    Srinivas, V., Mitra, P.: Applications of link prediction. Link Prediction in Social Networks. SCS, pp. 57–61. Springer, Cham (2016). Scholar
  11. 11.
    Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2014, pp. 701–710. ACM, New York (2014)Google Scholar
  12. 12.
    Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web. WWW 2015, pp. 1067–1077. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva (2015)Google Scholar
  13. 13.
    Chang, S., Han, W., Tang, J., Qi, G.J., Aggarwal, C.C., Huang, T.S.: Heterogeneous network embedding via deep architectures. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2015, pp. 119–128. ACM, New York (2015)Google Scholar
  14. 14.
    Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2016, pp. 855–864. ACM, New York (2016)Google Scholar
  15. 15.
    Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management. CIKM 2003, pp. 556–559. ACM, New York (2003)Google Scholar
  16. 16.
    Hasan, M.A., Zaki, M.J.: A survey of link prediction in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 243–275. Springer, Boston (2011)CrossRefGoogle Scholar
  17. 17.
    Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A: Stat. Mech. Appl. 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  20. 20.
    Network Developers: Link prediction algorithms (2017). Accessed 17 Jan 2018
  21. 21.
    Gao, F., Musial, K., Cooper, C., Tsoka, S.: Link prediction methods and their accuracy for different social networks and network metrics. Sci. Program. 2015, 1 (2015)Google Scholar
  22. 22.
    Robins, G., Snijders, T., Wang, P., Handcock, M., Pattison, P.: Recent developments in exponential random graph (p*) models for social networks. Soc. Netw. 29(2), 192–215 (2007)CrossRefGoogle Scholar
  23. 23.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27(1), 415–444 (2001)CrossRefGoogle Scholar
  24. 24.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  25. 25.
    Fire, M., Tenenboim-Chekina, L., Puzis, R., Lesser, O., Rokach, L., Elovici, Y.: Computationally efficient link prediction in a variety of social networks. ACM Trans. Intell. Syst. Technol. 5(1), 10:1–10:25 (2014)Google Scholar
  26. 26.
    Chen, H., Li, X., Huang, Z.: Link prediction approach to collaborative filtering. In: Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL 2005), pp. 141–142, June 2005Google Scholar
  27. 27.
    Liu, Y., Kou, Z.: Predicting who rated what in large-scale datasets. SIGKDD Explor. Newsl. 9(2), 62–65 (2007)CrossRefGoogle Scholar
  28. 28.
    Kossinets, G., Watts, D.J.: Origins of homophily in an evolving social network. Am. J. Sociol. 115(2), 405–450 (2009)CrossRefGoogle Scholar
  29. 29.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRefGoogle Scholar
  30. 30.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  31. 31.
    Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  32. 32.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, pp. 585–591 (2002)Google Scholar
  33. 33.
    Tang, L., Liu, H.: Leveraging social media networks for classification. Data Min. Knowl. Discov. 23(3), 447–478 (2011)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)CrossRefGoogle Scholar
  35. 35.
    Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. CIKM 2015, pp. 891–900. ACM, New York (2015)Google Scholar
  36. 36.
    Karl Pearson, F.R.S.: LIII. on lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901). Scholar
  37. 37.
    Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrika 1(3), 211–218 (1936)CrossRefGoogle Scholar
  38. 38.
    Torgerson, W.S.: Theory and Methods of Scaling. Wiley, Hoboken (1958)Google Scholar
  39. 39.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)zbMATHGoogle Scholar
  40. 40.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  41. 41.
    Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2016, pp. 1225–1234. ACM, New York (2016)Google Scholar
  42. 42.
    Carstens, B.T., Jensen, M.R., Spaniel, M.F., Hermansen, A.: Vertex similarity in graphs using feature learning (2017).
  43. 43.
    Wu, H., Lerman, K.: Network vector: distributed representations of networks with global context. arXiv preprint arXiv:1709.02448 (2017)
  44. 44.
    Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: IJCAI, pp. 2111–2117 (2015)Google Scholar
  45. 45.
    Huang, X., Li, J., Hu, X.: Label informed attributed network embedding. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. WSDM 2017, pp. 731–739. ACM, New York (2017)Google Scholar
  46. 46.
    Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. Network 11(9), 12 (2016)Google Scholar
  47. 47.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 1188–1196 (2014)Google Scholar
  48. 48.
    Liao, L., He, X., Zhang, H., Chua, T.S.: Attributed social network embedding. arXiv preprint arXiv:1705.04969 (2017)
  49. 49.
    Weston, J., Ratle, F., Mobahi, H., Collobert, R.: Deep learning via semi-supervised embedding. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 639–655. Springer, Heidelberg (2012). Scholar
  50. 50.
    Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. arXiv preprint arXiv:1603.08861 (2016)
  51. 51.
    Powered by HSE Portal: Publications of HSE (2017). Accessed 9 May 2017
  52. 52.
    Makarov, I., Bulanov, O., Zhukov, L.E.: Co-author recommender system. In: Kalyagin, V., Nikolaev, A., Pardalos, P., Prokopyev, O. (eds.) Models, Algorithms, and Technologies for Network Analysis. PROMS, vol. 197, pp. 251–257. Springer, Cham (2017). Scholar
  53. 53.
    Makarov, I., Bulanov, O., Gerasimova, O., Meshcheryakova, N., Karpov, I., Zhukov, L.E.: Scientific matchmaker: collaborator recommender system. In: van der Aalst, W.M., et al. (eds.) AIST 2017. LNCS, vol. 10716, pp. 404–410. Springer, Cham (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsMoscowRussia
  2. 2.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

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