Overview
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...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in neural information processing systems, pp 585–591
Brandes U, Delling D, Gaertler M, Görke R, Hoefer M, Nikoloski Z, Wagner D (2006) Maximizing modularity is hard. arXiv preprint physics/0608255
Cao S, Lu W, Xu Q (2016) Deep neural networks for learning graph representations. In: AAAI, pp 1145–1152
Chakrabarti D, Faloutsos C (2006) Graph mining: laws, generators, and algorithms. ACM Compu Surv (CSUR) 38(1):2
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 119–128
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 79–82
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 intelligence
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 855–864
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_3
Karypis G, Kumar V (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20(1):359–392
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781
Newman ME (2006a) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):036,104
Newman ME (2006b) Modularity and community structure in networks. Proc Natl Acad Sci 103(23): 8577–8582
Nowicki K, Snijders TAB (2001) Estimation and prediction for stochastic blockstructures. J Am Stat Assoc 96(455):1077–1087
Perozzi B (2016) Local modeling of attributed graphs: algorithms and applications. PhD thesis, State University of New York at Stony Brook
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 701–710
Rakesh V, Choo J, Reddy CK (2015) Project recommendation using heterogeneous traits in crowdfunding. In: ICWSM, pp 337–346
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 817–826
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 677–685
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 1067–1077
Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323
Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416
Zafarani R, Abbasi MA, Liu H (2014) Social media mining: an introduction. Cambridge University Press, New York
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this entry
Cite this entry
Rakesh, V., Tang, L., Liu, H. (2019). Feature Learning from Social Graphs. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_273
Download citation
DOI: https://doi.org/10.1007/978-3-319-77525-8_273
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77524-1
Online ISBN: 978-3-319-77525-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering