Advertisement

Sparse network embedding for community detection and sign prediction in signed social networks

  • Baofang Hu
  • Hong WangEmail author
  • Xiaomei Yu
  • Weihua Yuan
  • Tianwen He
Original Research

Abstract

Network embedding is an important pre-process for analysing large scale information networks. Several network embedding algorithms have been proposed for unsigned social networks. However, these methods cannot be simply migrate to signed social networks which have both positive and negative relationships. In this paper, we present our signed social network embedding model which is based on the word embedding model. To deal with two kinds of links, we define two relationships: neighbour relationship and common neighbour relationship, as well as design a bias random walk procedure. In order to further improve interpretation of the representation vectors, the follow-proximally-regularized-leader online learning algorithm is introduced to the traditional word embedding framework to acquire sparse representations. Extensive experiments were carried out to compare our algorithm with three state-of-the-art methods for community detection and sign prediction tasks. The experimental results demonstrate that our algorithm performs better than the comparison algorithms on most signed social networks.

Keywords

Signed social network Network embedding Word embedding Sparse representation Follow-proximally-regularized-leader 

Notes

Acknowledgements

This work is partly funded by the National Nature Science Foundation of China (nos. 61672329, 61373149, 61472233, 61572300, and 81273704), Shandong Provincial Project for Science and Technology Development (no. 2014GGX101026), Shandong Provincial Project of Education Scientific Plan (no. ZK1437B010), Taishan Scholar Program of Shandong Province (nos. TSHW201502038 and 20110819), and Shandong Provincial Project of Exquisite Course (nos. 2012BK294, 2013BK399, and 2013BK402).

References

  1. Andrade N, Andrade N, Pouwelse J, Sips H (2012) Leveraging trust and distrust for sybil-tolerant voting in online social media. The workshop on privacy and security in online social media (pp. 1). ACM.  https://doi.org/10.1145/2185354.2185355
  2. Chiang KY, Hsieh CJ, Natarajan N, Dhillon IS, Tewari A (2013) Prediction and clustering in signed networks: a local to global perspective. J Mach Learn Res 15(1):1177–1213 arXiv:1302.5145 MathSciNetzbMATHGoogle Scholar
  3. Davis JA (1967) Clustering and structural balance in graphs. Hum Relat 20(2):181–187.  https://doi.org/10.1177/001872676702000206 CrossRefGoogle Scholar
  4. Gmez S, Jensen P, Arenas A (2009) Analysis of community structure in networks of correlated data. Phys Rev E Stat Nonlinear Soft Matter Phys 80(2):016114.  https://doi.org/10.1103/PhysRevE. 80.016114 CrossRefGoogle Scholar
  5. Grover A, Leskovec J (2016) node2vec: Scalable Feature Learning for Networks: KDD. In: Proceedings international conference on knowledge discovery and data mining, 2016, pp 855–864.  https://doi.org/10.1145/2939 672.2939754
  6. Heider F (1946) Attitudes and cognitive organization. J Psychol 21(1):107.  https://doi.org/10.1080/00223980.1946.9917275
  7. Kunegis J, Schmidt S, Lommatzsch A, Lerner J, Luca EWD, Albayrak S (2010) Spectral analysis of signed graphs for clustering, prediction and visualization. In: Proceedings of the SIAM international conference on data mining, 2010, pp 559.  https://doi.org/10.1137/1.9781611972801.49
  8. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E Stat Nonlinear Soft Matter Phys 78(2):046110.  https://doi.org/10.1103/PhysRevE.78.046110 CrossRefGoogle Scholar
  9. Le QV, Mikolov T (2014) Distributed representations of sentences and documents. Comput Sci 4:1188–1196 arXiv:1405.4053 Google Scholar
  10. Leskovec J, Huttenlocher D, Kleinberg J (2010) Signed networks in social media. Sigchi Conf Hum Factors Comput Syst 2010:1361–1370 arXiv:1003.2424 Google Scholar
  11. Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links in online social networks. Int Conf World Wide Web 2010:641–650 arXiv:1003.2429 Google Scholar
  12. Leskovec J, Krevl A (2014) SNAP datasets: stanford large network dataset collection. urlhttp://snap.stan-ford.edu/data. Accessed 26 Jun 2014Google Scholar
  13. Levy O, Goldberg Y (2014) Neural word embedding as implicit matrix factorization. Adv Neural Inf Process Syst 3:2177–2185Google Scholar
  14. Levy O, Goldberg Y, Dagan I (2015) Improving distributional similarity with lessons learned from word embeddings. Bulletin De La Socit Botanique De France 75(3):552–555.  https://doi.org/10.1080/00378941. 1928.10836296 Google Scholar
  15. Li J, Liu Z, Chen X, Xhafa F, Tan X, Wong DS (2014) L-encdb: a lightweight framework for privacy-preserving data queries in cloud computing. Knowl Based Syst 79:18–26.  https://doi.org/10.1016/j.knosys.2014.04.010 CrossRefGoogle Scholar
  16. Li J, Yan H, Liu Z, Chen X (2015) Location-sharing systems with enhanced privacy in mobile online social networks. IEEE Syst J.  https://doi.org/10.1109/JSYST.2015.2415835
  17. Li J, Li J, Chen X, Jia C, Lou W (2015b) Identity-based encryption with outsourced revocation in cloud computing. Comput IEEE Trans 64(2):425–437.  https://doi.org/10.1109/TC.2013.208 MathSciNetCrossRefzbMATHGoogle Scholar
  18. Li J, Zhang Y, Chen X, Xiang Y (2018) Secure attribute-based data sharing for resource-limited users in cloud computing. Comput Secur 72:1–12.  https://doi.org/10.1016/j.cose.2017.08.007
  19. Liao L, He X, Zhang H, Chua TS (2017) Attributed social network embedding. arXiv: 1705.04969
  20. Liu X, Li S, Zhang K (2016) Optimal control of switching time in switched stochastic systems with multi-switching times and different costs. Int J Control.  https://doi.org/10.1080/00207 179.2016.1214879
  21. Luo C, Liu H (2014) Controllability of boolean control networks under asynchronous stochastic update with time delay. J Vib Control.  https://doi.org/10.1177/1077546314528022
  22. Mcmahan HB (2011) Follow-the-regularized-leader and mirror descent: equivalence theorems and l1 regularization. J Mach Learn Res Proc Track 15: 525–533Google Scholar
  23. Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26:3111–3119 arXiv:1310.4546 Google Scholar
  24. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298(5594):824–827.  https://doi.org/10.1126/science.298.5594.824 CrossRefGoogle Scholar
  25. Oda K, Kimura T, Matsuoka Y, Funahashi A, Muramatsu M, Kitano H (2004) Molecular interaction map of a macrophage. In: Proceedings of JSPE semestrial meeting the Japan society for precision engineering, 2004, pp 417Google Scholar
  26. Oda K, Matsuoka Y, Funahashi A, Kitano H (2005) A comprehensive pathway map of epidermal growth factor receptor signaling. Mol Syst Biol.  https://doi.org/10.1038/msb4100014
  27. Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. In: ACM SIG-KDD international conference on knowledge discovery and data mining, 2014, pp 701.  https://doi.org/10.1145/ 2623330.2623732
  28. Read KE (1954) Cultures of the central highlands, new guinea. Southwestern J Anthropol 10(1):1–43.  https://doi.org/10.1086/soutjanth.10.1.3629074 CrossRefGoogle Scholar
  29. Shen-Orr SS, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31(1):64–68.  https://doi.org/10.1038/ng881 CrossRefGoogle Scholar
  30. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) LINE: large-scale information network embedding. Int Conf World Wide Web 2015:1067–1077.  https://doi.org/10.1145/2736277.274 1093 Google Scholar
  31. Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: The ACM SIGKDD international conference, 2016, pp 1225–1234.  https://doi.org/10.1145/ 2939672.2939753
  32. Wang J, Gong B, Liu H, Li S (2015) Model and algorithm for heterogeneous scheduling integrated with energy-efficiency awareness. Trans Inst Meas Control.  https://doi.org/10.1177/0142331215583324
  33. Wang S, Tang J, Aggarwal C, Chang Y, Liu H (2017) Signed network embedding in social media. In: The SIAM international conference on data mining, pp 327–335Google Scholar
  34. Wu F, Huberman BA (2004) Finding communities in linear time: a physics approach. Eur Phys J B 38(2):331–338.  https://doi.org/10.1140/ epjb/e2004-00125-x CrossRefGoogle Scholar
  35. Xiao L (2010) Dual averaging methods for regularized stochastic learning and online optimization. J Mach Learn Res 11(1):2543–2596MathSciNetzbMATHGoogle Scholar
  36. Yu X, Wang H, Zheng X, Wang Y (2016) Effective algorithms for vertical mining probabilistic frequent patterns in uncertain mobile environments. Int J Ad Hoc Ubiquitous Comput 23(3/4):137–151.  https://doi.org/10.1504/IJAHUC.2016.10000377
  37. Yuan S, Wu X, Xiang Y (2017) Sne: signed network embedding. In: Pacific-Asia conference on knowledge discovery and data mining, 2017, pp 183–195.  https://doi.org/10.1007/978-3-319-57529-2_15
  38. Zhang Z, Liu H (2015) Social recommendation model combining trust propagation and sequential behaviors. Appl Intell 43(3):695–706.  https://doi.org/10.1007/s10489-015-0681-y CrossRefGoogle Scholar
  39. Zheng Q, Skillicorn DB (2015) Spectral embedding of signed networks. In: Proceedings of the 2015 SIAM international conference on data mining, 2015, pp 55-63.  https://doi.org/10.1137/1.9781611974010.7
  40. Zheng XW, Lu DJ, Wang XG, Liu H (2015) A cooperative coevolutionary biogeography-based optimizer. Appl Intell 43(1):95–111.  https://doi.org/10.1007/s10489-014-0627-9 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Baofang Hu
    • 1
    • 2
    • 3
  • Hong Wang
    • 1
    • 3
    Email author
  • Xiaomei Yu
    • 1
    • 3
  • Weihua Yuan
    • 1
    • 3
  • Tianwen He
    • 1
    • 3
  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanChina
  2. 2.School of Information TechnologyShandong Women’s UniversityJinanChina
  3. 3.Shandong Provincial Key Laboratory for Distributed Computer Software Novel TechnologyShandong Normal UniversityJinanChina

Personalised recommendations