Skip to main content
SpringerLink
Log in

Graph embedding based real-time social event matching for EBSNs recommendation

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Event-based social networks (EBSNs) are platforms for users to publish, organize, or choose to participate in events through social networks. In recent years, the number of users and events on EBSNs has increased dramatically, and interactions among them have become more complicated. This makes it difficult to model EBSN networks for effective analysis and mining. Moreover, the requirement of real-time matching between users and impromptu events according to impromptu spatio-temporal constraints is becoming urgent because of the significant dynamics brought by the widespread use of mobile devices on EBSNs. Therefore, graph embedding based real-time social event matching technologies for EBSNs are studied in this paper. We first model an EBSN as a heterogeneous information network, and perform graph embedding to represent the nodes in it, which can more effectively reflect the hidden features of nodes and contribute to mining users and events preferences. Then heuristic social event matching methods are employed to effectively find overall optimal recommendations between users and events under spatio-temporal constraints in real-time. This forms a two-stage framework, i.e., the representation learning stage and the real-time matching stage. We conducted experiments on the Meetup dataset to verify the effectiveness of the framework by combining different graph embedding methods and heuristic matching algorithms. The results show that the proposed framework yields improvements in the matching success rate, user satisfaction, and user waiting time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. https://www.meetup.com/

  2. https://www.plancast.com/

  3. https://www.douban.com/location

  4. https://techcrunch.com/2020/03/30/wework-sells-off-social-network-meetup-to-alleycorp-and-other-investors/

  5. https://scikit-opt.github.io/scikit-opt/.scikit-opt is a python module for heuristic algorithms.

References

  1. Ahmed, A., Shervashidze, N., Narayanamurthy, S., Josifovski, V., Smola, A. J.: Distributed large-scale natural graph factorization. In: Proceedings of the 22nd International Conference on World Wide Web, pp 37–48 (2013)

  2. Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada], . http://papers.nips.cc/paper/1961-laplacian-eigenmaps-and-spectral-techniques-for-embedding-and-clustering, pp 585–591. MIT Press (2001)

  3. Brand, M.: Continuous nonlinear dimensionality reduction by kernel eigenmaps. In: IJCAI, pp 547–554 (2003)

  4. 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, pp 891–900 (2015)

  5. Cao, S., Lu, W., Xu, Q.: Deep neural networks for learning graph representations. In: AAAI, vol. 16, pp 1145–1152 (2016)

  6. Chen, H., Perozzi, B., Hu, Y., Skiena, S.: Harp: Hierarchical representation learning for networks. arXiv:1706.07845 (2017)

  7. Cheng, Y., Yuan, Y., Chen, L., Giraud-Carrier, C., Wang, G., Li, B.: Event-participant and incremental planning over event-based social networks. IEEE Trans. Knowl. Data Eng. 33(2), 474–488 (2021). https://doi.org/10.1109/TKDE.2019.2931906

    Article  Google Scholar 

  8. Cheng, Y., Yuan, Y., Chen, L., Giraud-Carrier, C.G., Wang, G.: Complex event-participant planning and its incremental variant. In: 33rd IEEE International Conference on Data Engineering, ICDE 2017. https://doi.org/10.1109/ICDE.2017.135, pp 859–870 (2017 )

  9. Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3097983.3098036, pp 135–144. ACM (2017)

  10. Fu, G., Yuan, B., Duan, Q., Yao, X.: Representation learning for heterogeneous information networks via embedding events. arXiv:1901.10234 (2019)

  11. Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: A survey. Knowl. Based Syst. 151, 78–94 (2018). https://doi.org/10.1016/j.knosys.2018.03.022

    Article  Google Scholar 

  12. 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, pp. 855–864 (2016)

  13. He, X., Niyogi, P.: Locality preserving projections. In: Advances in neural information processing systems, pp. 153–160 (2004)

  14. Jiang, H., Zhou, R., Zhang, L., Wang, H., Zhang, Y.: Sentence level topic models for associated topics extraction. World Wide Web 22(6), 2545–2560 (2019)

    Article  Google Scholar 

  15. Jolliffe, I. T.: Principal components in regression analysis Principal Component Analysis, pp 129–155. Springer (1986)

  16. Kaufman, L., Rousseeuw, P. J.: Finding Groups in Data: an Introduction to Cluster Analysis, vol. 344. Wiley, Hoboken (2009)

    Google Scholar 

  17. Kipf, T. N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016)

  18. Kipf, T. N., Welling, M.: Variational graph auto-encoders. arXiv:1611.07308 (2016)

  19. Li, J., Zhu, J., Zhang, B.: Discriminative deep random walk for network classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1004–1013 (2016)

  20. Liao, Y., Lam, W., Bing, L., Shen, X.: Joint modeling of participant influence and latent topics for recommendation in event-based social networks. ACM Trans. Inf. Syst. 36(3), 29:1–29:31 (2018). https://doi.org/10.1145/3183712

    Article  Google Scholar 

  21. Liu, S., Wang, B., Xu, M.: Event recommendation based on graph random walking and history preference reranking. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/3077136.3080663, pp 861–864 (2017)

  22. Liu, X., He, Q., Tian, Y., Lee, W. C., McPherson, J., Han, J.: Event-based social networks: linking the online and offline social worlds. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1032–1040 (2012)

  23. Luo, D., Ding, C.H.Q., Nie, F., Huang, H., Cauchy graph embedding. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011. https://icml.cc/2011/papers/353_icmlpaper.pdf, pp 553–560. Omnipress (2011)

  24. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: 1st International Conference on Learning Representations, ICLR 2013, Workshop Track Proceedings. arXiv:1301.3781 (2013)

  25. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS’13, pp. 3111–3119 (2013)

  26. Mo, Y., Li, B., Wang, B., Yang, L.T., Xu, M.: Event recommendation in social networks based on reverse random walk and participant scale control. Future Gener. Comput. Syst. 79, 383–395 (2018). https://doi.org/10.1016/j.future.2017.02.045

    Article  Google Scholar 

  27. Nie, F., Zhu, W., Li, X.: Unsupervised large graph embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)

  28. Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1105–1114 (2016)

  29. Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. Network 11(9), 12 (2016)

    Google Scholar 

  30. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., VanderPlas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011). http://dl.acm.org/citation.cfm?id=2078195

    MathSciNet  MATH  Google Scholar 

  31. Peng, M., Zeng, G., Sun, Z., Huang, J., Wang, H., Tian, G.: Personalized app recommendation based on app permissions. World Wide Web 21(1), 89–104 (2018)

    Article  Google Scholar 

  32. 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, pp. 701–710 (2014)

  33. Perozzi, B., Kulkarni, V., Skiena, S.: Walklets: Multiscale graph embeddings for interpretable network classification. arXiv:1605.02115 (2016)

  34. Pham, T.N., Li, X., Cong, G., Zhang, Z.: A general graph-based model for recommendation in event-based social networks. In: 31st IEEE International Conference on Data Engineering, ICDE 2015, pp. 567–578. https://doi.org/10.1109/ICDE.2015.7113315 (2015)

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

    Article  Google Scholar 

  36. Shaw, B., Jebara, T.: Structure preserving embedding. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 937–944 (2009)

  37. She, J., Tong, Y., Chen, L.: Utility-aware social event-participant planning. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1629–1643. https://doi.org/10.1145/2723372.2749446 (2015)

  38. She, J., Tong, Y., Chen, L., Cao, C. C.: Conflict-aware event-participant arrangement. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 735–746. IEEE (2015)

  39. Skarding, J., Gabrys, B., Musial, K.: Foundations and modelling of dynamic networks using dynamic graph neural networks: A survey (2020)

  40. Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  41. Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4(11), 992–1003 (2011). http://www.vldb.org/pvldb/vol4/p992-sun.pdf

    Article  Google Scholar 

  42. Sun, Y., Norick, B., Han, J., Yan, X., Yu, P.S., Yu, X.: Pathselclus: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. ACM Trans. Knowl. Discov. Data 7 (3), 11:1–11:23 (2013). https://dl.acm.org/citation.cfm?id=2500492

    Article  Google Scholar 

  43. Tong, Y., Meng, R., She, J.: On bottleneck-aware arrangement for event-based social networks. In: 31st IEEE International Conference on Data Engineering Workshops, ICDE Workshops 2015, pp. 216–223. https://doi.org/10.1109/ICDEW.2015.7129579 (2015)

  44. 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, pp. 1225–1234 (2016)

  45. Wang, S., Hu, L., Wang, Y., He, X., Sheng, Q. Z., Orgun, M., Cao, L., Wang, N., Ricci, F., Yu, P. S.: Graph learning approaches to recommender systems: A review. arXiv:2004.11718 (2020)

  46. Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)

  47. Wei, X., Xu, L., Cao, B., Yu, P. S.: Cross view link prediction by learning noise-resilient representation consensus. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1611–1619 (2017)

  48. Wu, G., Li, X., Cui, K., Chen, Z., Qiao, B., Han, D., Xia, L.: A graph embedding based real-time social event matching model for ebsns recommendation. In: Web Information Systems Engineering - WISE 2020 - 21st International Conference, Proceedings, Part I, vol. 12342, pp. 41–55. https://doi.org/10.1007/978-3-030-62005-9∖_4(2020)

  49. Yang, Z., Cohen, W., Salakhudinov, R.: Revisiting semi-supervised learning with graph embeddings. In: International Conference on Machine Learning, pp 40–48. PMLR (2016)

  50. Yang, Z., Tang, J., Cohen, W.: Multi-modal bayesian embeddings for learning social knowledge graphs. arXiv:1508.00715 (2015)

  51. Zhou, C., Liu, Y., Liu, X., Liu, Z., Gao, J.: Scalable graph embedding for asymmetric proximity. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Gang Wu, Leilei Li, Xueyu Li, Yongzheng Chen, Zhiyong Chen, Baiyou Qiao, Donghong Han & Li Xia

  2. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China

    Gang Wu

Authors
  1. Gang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leilei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xueyu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongzheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiyong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Baiyou Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Donghong Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Li Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Wu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering 2020 Guest Editors: Hua Wang, Zhisheng Huang, and Wouter Beek

Supported by the National Key R&D Program of China (Grant No. 2019YFB1405300), the NSFC (Grant No.61872072 and No.61672144), and the State Key Laboratory of Computer Software New Technology Open Project Fund (Grant No. KFKT2018B05).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, G., Li, L., Li, X. et al. Graph embedding based real-time social event matching for EBSNs recommendation. World Wide Web 25, 335–356 (2022). https://doi.org/10.1007/s11280-021-00934-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-021-00934-y

Keywords

Search

Navigation