Skip to main content
SpringerLink
Log in

Link prediction in social networks using hyper-motif representation on hypergraph

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Link prediction, a critical pursuit in complex networks research, revolves around the predictive understanding of connections between nodes. Our novel approach introduces a hypergraph to model the network, diverging from the conventional “node–edge” structures. This departure involves the strategic mapping of hyper-motifs into open and closed structures, and the utilization of hyper-motifs as hyper-nodes. This paper proposes the learning embedding based on hyper-motif of the network (LEHMN) model to improve the link prediction process. This innovative framework aims at enhancing our capacity to discern and represent nuanced structural similarities between nodes that might elude traditional models. To further refine our approach, we introduce the depth and breadth motif random walk strategy. This strategy, designed with a consideration for both connectivity and structural similarity, excels in acquiring node sequences. We apply this method to both open and closed hyper-motif structures, emphasizing its versatility and effectiveness in capturing the intricate relationships inherent in the network. In the realm of experimental validation, our proposed model outshines state-of-the-art baselines when tested on diverse datasets. The results highlight significant improvements across various scenarios, underscoring the robustness and efficacy of our approach. This substantiates the pivotal role our method plays in advancing link prediction within the dynamic landscape of complex networks research.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Liu, J., Song, L., Wang, G., Shang, X.: Meta-HGT: metapath-aware HyperGraph transformer for heterogeneous information network embedding. Neural Netw. 157, 65–76 (2023)

    Article  Google Scholar 

  2. Zhao, Z., Yang, K., Guo, J.: Link prediction with hypergraphs via network embedding. Appl. Sci. 13(1), 523 (2022)

    Article  Google Scholar 

  3. Zhao, H., Wang, H., Niu, B., Zhao, X., Xu, N.: Adaptive fuzzy decentralized optimal control for interconnected nonlinear systems with unmodeled dynamics via mixed data and event driven method. Fuzzy Sets Syst. 474, 108735 (2024)

    Article  MathSciNet  Google Scholar 

  4. 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).

  5. Cheng, F., Niu, B., Xu, N., Zhao, X.: Resilient distributed secure consensus control for uncertain networked agent systems under hybrid DoS attacks. Commun. Nonlinear Sci. Numer. Simul. 129, 107689 (2024)

    Article  MathSciNet  Google Scholar 

  6. Zhao, Y., Liang, H., Zong, G., Wang, H.: Event-based distributed finite-horizon H∞ consensus control for constrained nonlinear multiagent systems. IEEE Syst. J. 17(4), 5369–5380 (2023)

    Article  Google Scholar 

  7. Wang, C., Chang, K.C.C., Wang, P., Qin, T., Guan, X.: Heterogeneous network crawling: reaching target nodes by motif-guided navigation. IEEE Trans. Knowl. Data Eng. 34(9), 4285–4297 (2020)

    Article  Google Scholar 

  8. Zhang, H., Zou, Q., Ju, Y., Song, C., Chen, D.: Distance-based support vector machine to predict DNA N6-methyladenine modification. Curr. Bioinform. 17(5), 473–482 (2022)

    Article  Google Scholar 

  9. Cao, C., Wang, J., Kwok, D., Cui, F., Zhang, Z., Zhao, D., et al.: webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study. Nucleic Acids Res. 50(D1), D1123–D1130 (2022)

    Article  Google Scholar 

  10. Xue, B., Yang, Q., Jin, Y., Zhu, Q., Lan, J., Lin, Y., et al.: Genotoxicity assessment of haloacetaldehyde disinfection byproducts via a simplified yeast-based toxicogenomics assay. Environ. Sci. Technol. 57(44), 16823–16833 (2023)

    Article  Google Scholar 

  11. Zhao, H., Zong, G., Wang, H., Zhao, X., Xu, N.: Zero-sum game-based hierarchical sliding-mode fault-tolerant tracking control for interconnected nonlinear systems via adaptive critic design. IEEE Trans. Autom. Sci. Eng. (2023). https://doi.org/10.1109/TASE.2023.3317902

    Article  Google Scholar 

  12. Berahmand, K., Nasiri, E., Forouzandeh, S., Li, Y.: A preference random walk algorithm for link prediction through mutual influence nodes in complex networks. J. King Saud Univ. Comput. Inf. Sci. 34(8), 5375–5387 (2022)

    Google Scholar 

  13. Yang, R., Jia, A., Hu, Q., Guo, X., Sun, M.: Particle size effect on water vapor sorption measurement of organic shale: one example from Dongyuemiao Member of Lower Jurassic Ziliujing Formation in Jiannan Area of China. Adv. Geo-Energy Res. 4(2), 207–218 (2020)

    Article  Google Scholar 

  14. Long, W., Xiao, Z., Wang, D., Jiang, H., Chen, J., Li, Y., Alazab, M.: Unified spatial-temporal neighbor attention network for dynamic traffic prediction. IEEE Trans. Veh. Technol. 72(2), 1515–1529 (2022)

    Article  Google Scholar 

  15. Sun, G., Zhang, Y., Yu, H., Du, X., Guizani, M.: Intersection fog-based distributed routing for V2V communication in urban vehicular ad hoc networks. IEEE Trans. Intell. Transp. Syst. 21(6), 2409–2426 (2019)

    Article  Google Scholar 

  16. Jiang, Y., Yang, Y., Xu, Y., Wang, E.: Spatial-temporal interval aware individual future trajectory prediction. IEEE Trans. Knowl. Data Eng. (2023). https://doi.org/10.1109/TKDE.2023.3332929

    Article  Google Scholar 

  17. Xu, Y., Wang, E., Yang, Y., Xiong, H.: GS2-RS: a generative approach for alleviating cold start and filter bubbles in recommender systems. IEEE Trans. Knowl. Data Eng. 36(2), 668–681 (2023)

    Google Scholar 

  18. Zheng, W., Gong, G., Tian, J., Lu, S., Wang, R., Yin, Z., et al.: Design of a modified transformer architecture based on relative position coding. Int. J. Comput. Intell. Syst. 16(1), 168 (2023)

    Article  Google Scholar 

  19. Newman, M.E.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)

    Article  Google Scholar 

  20. Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)

    Article  Google Scholar 

  21. Xiao, Z., Shu, J., Jiang, H., Lui, J.C., Min, G., Liu, J., Dustdar, S.: Multi-objective parallel task offloading and content caching in D2D-aided MEC networks. IEEE Trans. Mob. Comput. 22(11), 6599–6615 (2022)

    Google Scholar 

  22. 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)

  23. Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., & Jaiswal, S.: graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)

  24. Zhao, K., Jia, Z., Jia, F., Shao, H.: Multi-scale integrated deep self-attention network for predicting remaining useful life of aero-engine. Eng. Appl. Artif. Intell. 120, 105860 (2023)

    Article  Google Scholar 

  25. Shao, P., Yang, Y., Xu, S., Wang, C.: Network embedding via motifs. ACM Trans. Knowl. Discov. Data TKDD 16(3), 1–20 (2021)

    Google Scholar 

  26. Sizemore, A.E., Giusti, C., Kahn, A., Vettel, J.M., Betzel, R.F., Bassett, D.S.: Cliques and cavities in the human connectome. J. Comput. Neurosci. 44, 115–145 (2018)

    Article  MathSciNet  Google Scholar 

  27. Liu, W., Lü, L.: Link prediction based on local random walk. Europhys. Lett. 89(5), 58007 (2010)

    Article  Google Scholar 

  28. Wen, Y., Song, X., Yan, B., Yang, X., Wu, L., Leng, D., et al.: Multi-dimensional data integration algorithm based on random walk with restart. BMC Bioinf. 22(1), 1–22 (2021)

    Article  Google Scholar 

  29. Lu, J., Osorio, C.: A probabilistic traffic-theoretic network loading model suitable for large-scale network analysis. Transp. Sci. 52(6), 1509–1530 (2018)

    Article  Google Scholar 

  30. Jalili, M., Orouskhani, Y., Asgari, M., Alipourfard, N., Perc, M.: Link prediction in multiplex online social networks. R. Soc. Open Sci. 4(2), 160863 (2017)

    Article  MathSciNet  Google Scholar 

  31. Yang, R., Yang, C., Peng, X., Rezaeipanah, A.: A novel similarity measure of link prediction in multi-layer social networks based on reliable paths. Concurr. Comput. Pract. Exp. 34(10), e6829 (2022)

    Article  Google Scholar 

  32. Zhang, X., Abolfathi, A.: Development of FriendLink similarity metric for link prediction in weighted multiplex networks. Cybern. Syst. (2022). https://doi.org/10.1080/01969722.2022.2151177

    Article  Google Scholar 

  33. Mohammadian, E., Dastgerdi, M.E., Manshad, A.K., Mohammadi, A.H., Liu, B., Iglauer, S., Keshavarz, A.: Application of underbalanced tubing conveyed perforation in horizontal wells: a case study of perforation optimization in a giant oil field in Southwest Iran. Adv. Geo-Energy Res. 6, 296–305 (2022)

    Article  Google Scholar 

  34. Xiao, G., Liao, J., Tan, Z., Zhang, X., Zhao, X.: A two-stage framework for directed hypergraph link prediction. Mathematics 10(14), 2372 (2022)

    Article  Google Scholar 

  35. Zhao, H., Zong, G., Zhao, X., Wang, H., Xu, N., Zhao, N.: Hierarchical sliding-mode surface-based adaptive critic tracking control for nonlinear multiplayer zero-sum games via generalized fuzzy hyperbolic models. IEEE Trans. Fuzzy Syst. 31(11), 4010–4023 (2023)

    Article  Google Scholar 

  36. Xu, X.J., Deng, C., Zhang, L.J.: Hyperlink prediction via local random walks and Jensen-Shannon divergence. J. Stat. Mech: Theory Exp. 2023(3), 033402 (2023)

    Article  MathSciNet  Google Scholar 

  37. Kang, X., Li, X., Yao, H., Li, D., Jiang, B., Peng, X., et al.: Dynamic hypergraph neural networks based on key hyperedges. Inf. Sci. 616, 37–51 (2022)

    Article  Google Scholar 

  38. Liu, S., Niu, B., Karimi, H.R., Zhao, X.: Self-triggered fixed-time bipartite fault-tolerant consensus for nonlinear multiagent systems with function constraints on states. Chaos Solitons Fractals 178, 114367 (2024)

    Article  MathSciNet  Google Scholar 

  39. Fan, H., Zhang, F., Wei, Y., Li, Z., Zou, C., Gao, Y., Dai, Q.: Heterogeneous hypergraph variational autoencoder for link prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4125–4138 (2021)

    Google Scholar 

  40. Yang, Y., Li, X., Guan, Y., Wang, H., Kong, C., Jiang, J.: LHP: Logical hypergraph link prediction. Expert Syst. Appl. 222, 119842 (2023)

    Article  Google Scholar 

  41. Zhang, H., Mi, Y., Fu, Y., Liu, X., Zhang, Y., Wang, J., Tan, J.: Security defense decision method based on potential differential game for complex networks. Comput. Secur. 129, 103187 (2023)

    Article  Google Scholar 

  42. Sun, G., Zhang, Y., Liao, D., Yu, H., Du, X., Guizani, M.: Bus-trajectory-based street-centric routing for message delivery in urban vehicular ad hoc networks. IEEE Trans. Veh. Technol. 67(8), 7550–7563 (2018)

    Article  Google Scholar 

  43. Liu, Z., Feng, J., Uden, L.: Technology opportunity analysis using hierarchical semantic networks and dual link prediction. Technovation 128, 102872 (2023)

    Article  Google Scholar 

  44. Guo, T., Yuan, H., Wang, L., Wang, T.: Rate-distortion optimized quantization for geometry-based point cloud compression. J. Electron. Imaging 32(1), 013047–013047 (2023)

    Article  Google Scholar 

  45. Lu, J., Osorio, C.: On the analytical probabilistic modeling of flow transmission across nodes in transportation networks. Transp. Res. Rec. 2676(12), 209–225 (2022)

    Article  Google Scholar 

  46. Xuemin, Z., Ying, R., Zenggang, X., Haitao, D., Fang, X., Yuan, L.: Resource-constrained and socially selfish-based incentive algorithm for socially aware networks. J. Signal Process. Syst. (2023). https://doi.org/10.1007/s11265-023-01896-2

    Article  Google Scholar 

  47. Benson, A.R., Abebe, R., Schaub, M.T., Jadbabaie, A., Kleinberg, J.: Simplicial closure and higher-order link prediction. Proc. Natl. Acad. Sci. 115(48), E11221–E11230 (2018)

    Article  Google Scholar 

  48. Service Centre of Huiyuan Sharing Academic Resources. University library dataset. http://hdl.handle.net/20.500.12291/10022. Accessed 12 Jan 2024.

  49. Leskovec, J., Huttenlocher, D., & Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th international conference on World wide web, pp 641–650 (2010)

  50. Xu, G., Zhang, Q., Song, Z., Ai, B.: Relay-assisted deep space optical communication system over coronal fading channels. IEEE Trans. Aerosp. Electron. Syst. 59(6), 8297–8312 (2023)

    Article  Google Scholar 

  51. Chen, J., Xu, M., Xu, W., Li, D., Peng, W., Xu, H.: A flow feedback traffic prediction based on visual quantified features. IEEE Trans. Intell. Transp. Syst. 24(9), 10067–10075 (2023)

    Article  Google Scholar 

  52. Jiang, H., Wang, M., Zhao, P., Xiao, Z., Dustdar, S.: A utility-aware general framework with quantifiable privacy preservation for destination prediction in LBSs. IEEE/ACM Trans. Networking 29(5), 2228–2241 (2021)

    Article  Google Scholar 

  53. Wang, Q., Hu, J., Wu, Y., Zhao, Y.: Output synchronization of wide-area heterogeneous multi-agent systems over intermittent clustered networks. Inf. Sci. 619, 263–275 (2023)

    Article  Google Scholar 

  54. Khayatnezhad, M., Fataei, E., Imani, A.: Integrated modeling of food–water–energy nexus for maximizing water productivity. Water Supply 23(3), 1362–1374 (2023)

    Article  Google Scholar 

  55. Xing, J., Yuan, H., Hamzaoui, R., Liu, H., Hou, J.: GQE-net: a graph-based quality enhancement network for point cloud color attribute. IEEE Trans. Image Process. 32, 6303–6317 (2023)

    Article  Google Scholar 

  56. Luo, R., Peng, Z., Hu, J., Ghosh, B.K.: Adaptive optimal control of affine nonlinear systems via identifier–critic neural network approximation with relaxed PE conditions. Neural Netw. 167, 588–600 (2023)

    Article  Google Scholar 

  57. Chen, J., Wang, Q., Peng, W., Xu, H., Li, X., Xu, W.: Disparity-based multiscale fusion network for transportation detection. IEEE Trans. Intell. Transp. Syst. 23(10), 18855–18863 (2022)

    Article  Google Scholar 

  58. Guo, H., Gu, W., Khayatnezhad, M., Ghadimi, N.: Parameter extraction of the SOFC mathematical model based on fractional order version of dragonfly algorithm. Int. J. Hydrogen Energy 47(57), 24059–24068 (2022)

    Article  Google Scholar 

  59. Shahidinejad, A., Abawajy, J.: An All-Inclusive Taxonomy and Critical Review of Blockchain-Assisted Authentication and Session Key Generation Protocols for IoT. ACM Comput. Surv. (2024). https://dl.acm.org/doi/abs/10.1145/3645087

  60. Jannesari, V., Keshvari, M., Berahmand, K.: A novel nonnegative matrix factorization-based model for attributed graph clustering by incorporating complementary information.Expert Syst. Appl. 242, 122799 (2023)

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. School of Computer and Software Engineering, Shandong College of Electronic Technology, Jinan, 250200, Shandong, China

    ChunYan Meng

  2. Chemical Engineering, Chemical & Petroleum Engineering Department, Sharif University of Technology, Tehran, Iran

    Hooman Motevalli

Authors
  1. ChunYan Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hooman Motevalli
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript.

Corresponding author

Correspondence to ChunYan Meng.

Ethics declarations

Conflict of interests

The authors declare no competing interests.

Ethics approval

Not applicable.

Additional information

Communicated by P. Pala.

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meng, C., Motevalli, H. Link prediction in social networks using hyper-motif representation on hypergraph. Multimedia Systems 30, 123 (2024). https://doi.org/10.1007/s00530-024-01324-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00530-024-01324-w

Keywords

Search

Navigation