Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Feature Learning from Social Graphs

  • Vineeth RakeshEmail author
  • Lei TangEmail author
  • Huan LiuEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_273


The rapid evolution of social network platforms such as Twitter, Facebook, and Instagram has resulted in heterogeneous networks with complex social interactions. Despite providing a rich source of information, the dimensionality of features in these networks poses several challenges to machine learning tasks such as personalization, prediction, and recommendation. Therefore, it is important to ask the question “how to capture such complex interactions between users in simplified dimensions?”. To answer this question, this entry explores network representation learning (NRL), where the objective is to model the complex high-dimensional interactions between nodes in a reduced feature space while simultaneously capturing the neighborhood similarity and community membership.

Information in the modern world flows in the form of graphs such as social networks, biological networks, and World Wide Web. These graphical structures reveal many intriguing characteristics about the...
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  1. Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in neural information processing systems, pp 585–591Google Scholar
  2. Brandes U, Delling D, Gaertler M, Görke R, Hoefer M, Nikoloski Z, Wagner D (2006) Maximizing modularity is hard. arXiv preprint physics/0608255Google Scholar
  3. Cao S, Lu W, Xu Q (2016) Deep neural networks for learning graph representations. In: AAAI, pp 1145–1152Google Scholar
  4. Chakrabarti D, Faloutsos C (2006) Graph mining: laws, generators, and algorithms. ACM Compu Surv (CSUR) 38(1):2CrossRefGoogle Scholar
  5. Chang S, Han W, Tang J, Qi GJ, Aggarwal CC, Huang TS (2015) Heterogeneous network embedding via deep architectures. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 119–128Google Scholar
  6. Chen CM, Tsai MF, Lin YC, Yang YH (2016) Query-based music recommendations via preference embedding. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp 79–82Google Scholar
  7. Fang H, Wu F, Zhao Z, Duan X, Zhuang Y, Ester M (2016) Community-based question answering via heterogeneous social network learning. In: Thirtieth AAAI conference on artificial intelligenceGoogle Scholar
  8. Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 855–864Google Scholar
  9. Jones I, Tang L, Liu H (2015) Community discovery in multi-mode networks. Springer International Publishing, pp 55–74. https://doi.org/10.1007/978-3-319-23835-7_3Google Scholar
  10. Karypis G, Kumar V (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20(1):359–392MathSciNetzbMATHCrossRefGoogle Scholar
  11. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781Google Scholar
  12. Newman ME (2006a) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):036,104MathSciNetCrossRefGoogle Scholar
  13. Newman ME (2006b) Modularity and community structure in networks. Proc Natl Acad Sci 103(23): 8577–8582CrossRefGoogle Scholar
  14. Nowicki K, Snijders TAB (2001) Estimation and prediction for stochastic blockstructures. J Am Stat Assoc 96(455):1077–1087MathSciNetzbMATHCrossRefGoogle Scholar
  15. Perozzi B (2016) Local modeling of attributed graphs: algorithms and applications. PhD thesis, State University of New York at Stony BrookGoogle Scholar
  16. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 701–710Google Scholar
  17. Rakesh V, Choo J, Reddy CK (2015) Project recommendation using heterogeneous traits in crowdfunding. In: ICWSM, pp 337–346Google Scholar
  18. Tang L, Liu H (2009) Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 817–826Google Scholar
  19. Tang L, Liu H, Zhang J, Nazeri Z (2008) Community evolution in dynamic multi-mode networks. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 677–685Google Scholar
  20. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th international conference on World Wide Web, international World Wide Web conferences steering committee, pp 1067–1077Google Scholar
  21. Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRefGoogle Scholar
  22. Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416MathSciNetCrossRefGoogle Scholar
  23. Zafarani R, Abbasi MA, Liu H (2014) Social media mining: an introduction. Cambridge University Press, New YorkCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Data Mining and Machine Learning Lab, School of Computing, Informatics, and Decision Systems EngineeringArizona State UniversityTempeUSA
  2. 2.Clari Inc.SunnyvaleUSA