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Representation Learning for Large-Scale Dynamic Networks

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Database Systems for Advanced Applications (DASFAA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10828))

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Abstract

Representation leaning on networks aims to embed networks into a low-dimensional vector space, which is useful in many tasks such as node classification, network clustering, link prediction and recommendation. In reality, most real-life networks constantly evolve over time with various kinds of changes to the network structure, e.g., creation and deletion of edges. However, existing network embedding methods learn the representation vectors for nodes in a static manner, which are not suitable for dynamic network embedding. In this paper, we propose a dynamic network embedding approach for large-scale networks. The method incrementally updates the embeddings by considering the changes of the network structures and is able to dynamically learn the embedding for networks with millions of nodes within a few seconds. Extensive experimental results on three real large-scale networks demonstrate the efficiency and effectiveness of our proposed methods.

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Notes

  1. 1.

    Available at http://socialcomputing.asu.edu/pages/datasets.

  2. 2.

    Available at https://aminer.org/citation.

  3. 3.

    Available at http://www.csie.ntu.edu.tw/~cjlin/liblinear/.

References

  1. Aggarwal, C., Subbian, K.: Evolutionary network analysis: a survey. ACM Comput. Surv. (CSUR) 47(1), 10 (2014)

    Article  Google Scholar 

  2. Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Proceedings of NIPS, pp. 585–591 (2002)

    Google Scholar 

  3. Bhagat, S., Cormode, G., Muthukrishnan, S.: Node classification in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 115–148. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-8462-3_5

    Chapter  Google Scholar 

  4. Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of CIKM, pp. 891–900. ACM (2015)

    Google Scholar 

  5. Chang, S., Han, W., Tang, J., Qi, G.J., Aggarwal, C.C., Huang, T.S.: Heterogeneous network embedding via deep architectures. In: Proceedings of SIGKDD, pp. 119–128. ACM (2015)

    Google Scholar 

  6. Chen, C., Tong, H.: Fast eigen-functions tracking on dynamic graphs. In: Proceedings of SDM, pp. 559–567. SIAM (2015)

    Google Scholar 

  7. Chen, J., Zhang, Q., Huang, X.: Incorporate group information to enhance network embedding. In: Proceedings of CIKM, pp. 1901–1904. ACM (2016)

    Google Scholar 

  8. Chen, M., Yang, Q., Tang, X.: Directed graph embedding. In: IJCAI, pp. 2707–2712 (2007)

    Google Scholar 

  9. Chen, Y., Wang, C.: HINE: heterogeneous information network embedding. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 180–195. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55753-3_12

    Chapter  Google Scholar 

  10. Cox, T.F., Cox, M.A.: Multidimensional Scaling. CRC Press, Boca Raton (2000)

    Book  Google Scholar 

  11. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9(Aug), 1871–1874 (2008)

    MATH  Google Scholar 

  12. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  13. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of SIGKDD, pp. 855–864. ACM (2016)

    Google Scholar 

  14. Huang, X., Li, J., Hu, X.: Label informed attributed network embedding. In: Proceedings of WSDM, pp. 731–739. ACM (2017)

    Google Scholar 

  15. Jian, L., Li, J., Liu, H.: Toward online node classification on streaming networks. Data Mining Knowl. Discov. 32, 1–27 (2017)

    MathSciNet  Google Scholar 

  16. Li, A.Q., Ahmed, A., Ravi, S., Smola, A.J.: Reducing the sampling complexity of topic models. In: Proceedings of SIGKDD, pp. 891–900. ACM (2014)

    Google Scholar 

  17. Li, C., Li, Z., Wang, S., Yang, Y., Zhang, X., Zhou, J.: Semi-supervised network embedding. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 131–147. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55753-3_9

    Chapter  Google Scholar 

  18. Li, J., Dani, H., Hu, X., Tang, J., Chang, Y., Liu, H.: Attributed network embedding for learning in a dynamic environment. arXiv preprint arXiv:1706.01860 (2017)

  19. Li, J., Hu, X., Jian, L., Liu, H.: Toward time-evolving feature selection on dynamic networks. In: Proceedings of ICDM, pp. 1003–1008. IEEE (2016)

    Google Scholar 

  20. Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)

    Article  Google Scholar 

  21. Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)

    MATH  Google Scholar 

  22. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  23. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp. 3111–3119 (2013)

    Google Scholar 

  24. Ning, H., Xu, W., Chi, Y., Gong, Y., Huang, T.: Incremental spectral clustering with application to monitoring of evolving blog communities. In: Proceedings of SDM, pp. 261–272. SIAM (2007)

    Google Scholar 

  25. Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of SIGKDD, pp. 701–710. ACM (2014)

    Google Scholar 

  26. Recht, B., Ré, C., Wright, S.J., Niu, F.: HOGWILD: a lock-free approach to parallelizing stochastic gradient descent. In: Proceedings of NIPS, pp. 693–701 (2011)

    Google Scholar 

  27. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

    Article  Google Scholar 

  28. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of WWW, pp. 1067–1077. ACM (2015)

    Google Scholar 

  29. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: Proceedings of SIGKDD, pp. 990–998. ACM (2008)

    Google Scholar 

  30. Tang, L., Liu, H.: Scalable learning of collective behavior based on sparse social dimensions. In: Proceedings of CIKM, pp. 1107–1116. ACM (2009)

    Google Scholar 

  31. Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  32. Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of SIGKDD, pp. 1225–1234. ACM (2016)

    Google Scholar 

  33. Wang, H., Li, Z.: Region representation learning via mobility flow. In: Proceedings of CIKM. ACM (2017)

    Google Scholar 

  34. Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Proceedings of AAAI, pp. 203–209 (2017)

    Google Scholar 

  35. Xu, L., Wei, X., Cao, J., Yu, P.S.: Embedding of embedding (EOE): joint embedding for coupled heterogeneous networks. In: Proceedings of WSDM, pp. 741–749. ACM (2017)

    Google Scholar 

  36. 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 

  37. Yu, X., Ren, X., Sun, Y., Gu, Q., Sturt, B., Khandelwal, U., Norick, B., Han, J.: Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of WSDM, pp. 283–292. ACM (2014)

    Google Scholar 

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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China under grant Nos. 61773331 and 61403328, the National Science Foundation under grant Nos. 1544455, 1652525, and 1618448, and the China Scholarship Council under grant No. 201608370018.

Author information

Authors and Affiliations

  1. College of Information and Science Technology, Pennsylvania State University, State College, USA

    Yanwei Yu, Huaxiu Yao, Hongjian Wang, Xianfeng Tang & Zhenhui Li

  2. School of Computer and Control Engineering, Yantai University, Yantai, China

    Yanwei Yu

Authors
  1. Yanwei Yu
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  2. Huaxiu Yao
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  3. Hongjian Wang
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  4. Xianfeng Tang
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  5. Zhenhui Li
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Corresponding author

Correspondence to Yanwei Yu .

Editor information

Editors and Affiliations

  1. Simon Fraser University, Burnaby, BC, Canada

    Jian Pei

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

  3. University of Queensland, Brisbane, QLD, Australia

    Shazia Sadiq

  4. University of Western Australia, Crawley, WA, Australia

    Jianxin Li

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Yu, Y., Yao, H., Wang, H., Tang, X., Li, Z. (2018). Representation Learning for Large-Scale Dynamic Networks. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_32

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  • DOI: https://doi.org/10.1007/978-3-319-91458-9_32

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  • Online ISBN: 978-3-319-91458-9

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