Skip to main content
SpringerLink
Log in

Group homophily based facility location selection in geo-social networks

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Conditional p-center problem is one of the classical facility location problems, which aims to find p facilities meeting the given distance condition with q pre-existing facilities. It is worth noting that, with the proliferation of the social network, the effect of social factor is non-negligible. However, the state-of-the-art solutions for the conditional p-center problem fail to deal with the social-aware scenario, and the key challenge is that the existing methods are incompatible with integrating the social condition. In this paper, we first formalize the conditional p-center problem in geo-social networks (GSCpC). To tackle this problem, we develop a homophily-based relaxation algorithm by considering both social constraint and spatial constraint. Specifically, in the first phase, we propose a partition algorithm based on Voronoi diagram to conquer the social constraint. Next, in the second phase, we propose a heuristic algorithm to refine the query results by minimizing the distance. To accelerate the performance, we also propose a category-aware user grouping strategy and a spatial distance based strategy to prune the unpromising results significantly. Experimental results on real-world datasets demonstrate that our approaches are more efficient and effective compared to the benchmarks.

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
Fig. 7

Similar content being viewed by others

Notes

  1. Homophily is a group concept that reflects the preference of the users in a group. Assigning the users with a higher homophily score to the same facility will progress connected people more similar.

  2. http://snap.stanford.edu/data/index.html

  3. https://developer.foursquare.com/

References

  1. Aboolian, R., Berman, O., Krass, D.: Optimizing facility location and design. Eur. J. Oper. Res. 289(1), 31–43 (2021)

    Article  MATH  Google Scholar 

  2. Afify, B., Soeanu, A., Awasthi, A.: Separation linearization approach for the capacitated facility location problem under disruption. Expert Syst. Appl. 169, 114187 (2021)

    Article  Google Scholar 

  3. Amir, A., Efrat, A., Myllymaki, J., Palaniappan, L., Wampler, K.: Buddy tracking-efficient proximity detection among mobile friends. Pervasive and Mobile Computing 3(5), 489–511 (2007)

    Article  Google Scholar 

  4. Aneja, Y.P., Chandrasekaran, R., Nair, K.P.K.: A note on the m -center problem with rectilinear distances. European Journal of Operational Research 35(1), 118–123 (1988)

    Article  MATH  Google Scholar 

  5. Armenatzoglou, N., Papadopoulos, S., Papadias, D.: A general framework for geo-social query processing. Proceedings of the VLDB Endowment 6(10), 913–924 (2013)

    Article  Google Scholar 

  6. Aziz, H., Chan, H., Lee, B., Li, B., Walsh, T.: Facility location problem with capacity constraints: Algorithmic and mechanism design perspectives. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 1806–1813. AAAI Press (2020)

  7. Babel, L., Kellerer, H., Kotov, V.: The k -partitioning problem. Mathematical Methods of Operations Research 47(1), 59–82 (1998)

    Article  MATH  Google Scholar 

  8. Berman, O., Drezner, Z.: A new formulation for the conditional p-median and p-center problems. Elsevier Science Publishers B. V. (2008)

  9. Berman, O., Simchi-Levi, D.: Conditional location problems on networks. Transportation Science 24(1), 77–78 (1990)

    Article  MATH  Google Scholar 

  10. CAI, T., Li, J., Mian, A.S., li, R., Sellis, T., Yu, J.X.: Target-aware holistic influence maximization in spatial social networks. IEEE Trans. Knowl. Data Eng., 1–1 (2020). https://doi.org/10.1109/TKDE.2020.3003047

  11. Chakrabarty, D., Goyal, P., Krishnaswamy, R.: The non-uniform k-center problem. ACM Trans. Algorithms 16(4), 46:1-46:19 (2020)

    Article  MATH  Google Scholar 

  12. Chen, J., Zhong, M., Li, J., Wang, D., Qian, T., Tu, H.: Effective deep attributed network representation learning with topology adapted smoothing. IEEE Transactions on Cybernetics (2021). https://doi.org/10.1109/TCYB.2021.306492

  13. Chen, D., Chen, R.: A relaxation-based algorithm for solving the conditional p-center problem. Operations Research Letters 38(3), 215–217 (2010)

    Article  MATH  Google Scholar 

  14. Chen, R., Handler, Y.: The conditional p-center problem in the plane. Naval Research Logistics 40(1), 117–127 (1993)

    Article  MATH  Google Scholar 

  15. Cornuéjols, G., Nemhauser, G.L., Wolsey, L.A.: The uncapacitated facility location problem. Tech. rep, Carnegie-mellon univ pittsburgh pa management sciences research group (1983)

  16. Drezner, Z.: On the rectangular p-center problem. Naval Research Logistics 34(2), 229–234 (1987)

    Article  MATH  Google Scholar 

  17. Drezner, Z.: Conditional p-center problems. Transportation Science 23(1), 51–53 (1989)

    Article  MATH  Google Scholar 

  18. Du, J., Michalska, S., Subramani, S., Wang, H., Zhang, Y.: Neural attention with character embeddings for hay fever detection from twitter. Health Inf. Sci. Syst. 7(1), 21 (2019)

    Article  Google Scholar 

  19. Francis, R., Mcginnis, F., White, J.: Facility layout and location. Prentice-Hall (1974)

  20. Gabriel, Y.: Location on networks theory and algorithms. MIT Press, Cambridge, Mass.-London (1979)

    MATH  Google Scholar 

  21. Garfinkel, R.S., Neebe, A.W., Rao, M.R.: The m-center problem: Minimax facility location. Management Science 23(10), 1133–1142 (1977)

    Article  MATH  Google Scholar 

  22. Haldar, N.A.H., Reynolds, M., Shao, Q., Paris, C., Li, J., Chen, Y.: Activity location inference of users based on social relationship. World Wide Web 24(4), 1165–1183 (2021)

    Article  Google Scholar 

  23. He, J., Rong, J., Sun, L., Wang, H., Zhang, Y., Ma, J.: A framework for cardiac arrhythmia detection from iot-based ecgs. World Wide Web 23(5), 2835–2850 (2020)

    Article  Google Scholar 

  24. Huang, Q., Liu, Y.: On geo-social network services. In: 2009 17th International Conference on Geoinformatics, pp. 1–6 (2009)

  25. 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 

  26. Kariv, O., Hakimi, S.L.: An algorithmic approach to network location problems. i: The p-centers. SIAM Journal on Applied Mathematics 37(3), 513–538 (1979)

  27. Li, J., Cai, T., Deng, K., Wang, X., Sellis, T., Xia, F.: Community-diversified influence maximization in social networks. Inf. Syst. 92, 101522 (2020)

    Article  Google Scholar 

  28. Li, Z., Wang, X., Li, J., Zhang, Q.: Deep attributed network representation learning of complex coupling and interaction. Knowl. Based Syst. 212, 106618 (2021)

    Article  Google Scholar 

  29. Liu, W., Sun, W., Chen, C., Huang, Y., Jing, Y., Chen, K.: Circle of friend query in geo-social networks. In: International Conference on Database Systems for Advanced Applications, pp. 126–137. Springer (2012)

  30. Ma, Y., Yuan, Y., Wang, G., Bi, X., Wang, Y.: Personalized geo-social group queries in location-based social networks. In: International Conference on Database Systems for Advanced Applications, pp. 388–405. Springer (2018)

  31. Ma, Y., Yuan, Y., Wang, G., Bi, X., Qin, H.: Trust-aware personalized route query using extreme learning machine in location-based social networks. Cognitive Computation 10(6), 965–979 (2018)

    Article  Google Scholar 

  32. Melo, M.T., Nickel, S., Saldanha-Da-Gama, F.: Facility location and supply chain management-a review. European Journal of Operational Research 196(2), 401–412 (2009)

    Article  MATH  Google Scholar 

  33. Nagarajan, C., Williamson, D.P.: Offline and online facility leasing. In: International Conference on Integer Programming and Combinatorial Optimization, pp. 303–315. Springer (2008)

  34. San Felice, M.C., Williamson, D.P., Lee, O.: The online connected facility location problem. In: Latin American Symposium on Theoretical Informatics, pp. 574–585. Springer (2014)

  35. Sarki, R., Ahmed, K., Wang, H., Zhang, Y.: Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf. Sci. Syst. 8(1), 32 (2020)

    Article  Google Scholar 

  36. Supriya, Siuly, S., Wang, H., Zhang, Y.: Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf. Sci. Syst. 8(1), 33 (2020)

  37. Watson-Gandy, C.D.T.: The multi-facility min-max weber problem. European Journal of Operational Research 18(1), 44–50 (1984)

    Article  MATH  Google Scholar 

  38. Xue, G., Zhong, M., Li, J., Chen, J., Zhai, C., Kong, R.: Dynamic network embedding survey. arxiv: abs/2103.15447 (2021). Accessed 9 Oct 2021

  39. Yang, D.N., Shen, C.Y., Lee, W.C., Chen, M.S.: On socio-spatial group query for location-based social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 949–957 (2012)

  40. Yang, Y., Guan, Z., Li, J., Zhao, W., Cui, J., Wang, Q.: Interpretable and efficient heterogeneous graph convolutional network. IEEE Transactions on Knowledge and Data Engineering Early Access (2021). https://doi.org/10.1109/TKDE.2021.3101356

    Article  Google Scholar 

  41. Yin, J., Tang, M., Cao, J., Wang, H., You, M., Lin, Y.: Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web, 1–23 (2021)

  42. Yin, H., Yang, S., Song, X., Liu, W., Li, J.: Deep fusion of multimodal features for social media retweet time prediction. World Wide Web 24(4), 1027–1044 (2021)

    Article  Google Scholar 

  43. Yiu, M.L., Šaltenis, S., Tzoumas, K., et al.: Efficient proximity detection among mobile users via self-tuning policies. Proceedings of the VLDB Endowment 3(1–2), 985–996 (2010)

    Article  Google Scholar 

  44. Zhang, Q., Lü, Z., Su, Z., Li, C., Fang, Y., Ma, F.: Vertex weighting-based tabu search for p-center problem. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 1481–1487. ijcai.org (2020)

  45. Zhang, F., Wang, Y., Liu, S., Wang, H.: Decision-based evasion attacks on tree ensemble classifiers. World Wide Web 23(5), 2957–2977 (2020)

    Article  Google Scholar 

  46. Zheng, Y., Zhang, L., Ma, Z., Xie, X., Ma, W.: Recommending friends and locations based on individual location history. ACM Trans. Web 5(1), 5:1-5:44 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

The work is partially supported by the National Nature Science Foundation of China (No. 62002054), Industry-university-research Innovation Fund for Chinese Universities (No. 2020ITA03009), China Postdoctoral Science Foundation (No. 2020M67 0780), and Postdoctoral Research Foundation of Northeast University.

Author information

Authors and Affiliations

  1. School of Business Administration, Northeastern University, Shenyang, China

    Yuliang Ma & Zhong-Zhong Jiang

  2. School of Computer Science and Technology, Anhui University, Hefei, China

    Ningning Cui

  3. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Ye Yuan & Guoren Wang

Authors
  1. Yuliang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ningning Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhong-Zhong Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ye Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guoren Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ningning Cui.

Ethics declarations

Conflicts of interest

The authors declare no conflicts of interest.

Ethical standard

This article does not contain any studies involving human participants and/or animals by any of the authors.

Consent to participate

Informed consent was obtained from all individual participants.

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 Decision Making in Heterogeneous Network Data Scenarios and Applications

Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, Y., Cui, N., Jiang, ZZ. et al. Group homophily based facility location selection in geo-social networks. World Wide Web 26, 33–53 (2023). https://doi.org/10.1007/s11280-022-01008-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-022-01008-3

Keywords

Search

Navigation