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Article

Multi-task Learning of Heterogeneous Hypergraph Representations in LBSNs

Published: 15 December 2024 Publication History

Abstract

Location-based service networks (LBSNs) have emerged as a primary source for numerous applications that attempt to understand human mobility and analyze social networks. However, mainstream studies on representation learning often consider LBSNs to be either regular graphs or a mixture of regular graphs and hypergraphs. As a potential solution, hypergraph convolution has recently emerged as a way to capture the structural context of hypergraphs. But applying this type of convolution to representation learning in an LBSN is challenging due to the inherent heterogeneity of LSBNs. In this paper, we address the inherent heterogeneity of LBSNs and enhance the performance of representation learning by leveraging the power of multi-task learning. By jointly optimizing both friendship prediction and POI recommendation tasks, our proposed framework, MH2-LBSN, effectively leverages the complementary information present in these tasks to learn more informative and robust representations. Extensive experiments with four real-world datasets against several state-of-the-art embedding methods validate the performance of MH2-LBSN

References

[1]
An, Y., Zong, C., Li, R., Qiu, T., Zhang, A., Zhu, R.: Searching user community and attribute location cluster in location-based social networks. In: ADMA, pp. 389–404 (2023)
[2]
Bai S, Zhang F, and Torr PH Hypergraph convolution and hypergraph attention Pattern Recogn. 2021 110 107637
[3]
Bao, J., Zheng, Yu., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. GeoInformatica 19(3), 525–565 (2015)
[4]
Cui P, Wang X, Pei J, and Zhu W A survey on network embedding TKDE 2018 31 5 833-852
[5]
Deng B, Yang D, Qu B, Fankhauser B, and Cudre-Mauroux P Robust location prediction over sparse spatiotemporal trajectory data: flashback to the right moment! TIST 2023 14 5 1-24
[6]
Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: AAAI. vol. 33, pp. 3558–3565 (2019)
[7]
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD, pp. 855–864 (2016)
[8]
Han, F., Wang, S., Zhao, J., Wu, R., Rui, X., Wang, Z.: Fair re-ranking recommendation based on debiased multi-graph representations. In: ADMA, pp. 168–182 (2023)
[9]
Ho, V.L., Ho, N., Pedersen, T.B.: Mining seasonal temporal patterns in time series. In: ICDE, pp. 2249–2261 (2023)
[10]
Jo, J., Baek, J., Lee, S., Kim, D., Kang, M., Hwang, S.J.: Edge representation learning with hypergraphs. In: NIPS, vol. 34 (2021)
[11]
Ju, W., et al.: Kernel-based substructure exploration for next poi recommendation. In: ICDM, pp. 221–230 (2022)
[12]
Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: KDD, pp. 1105–1114 (2016)
[13]
Qin, Y., et al.: DisenPOI: disentangling sequential and geographical influence for point-of-interest recommendation. In: WSDM, pp. 508–516 (2023)
[14]
Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., Tang, J.: Network embedding as matrix factorization: unifying DeepWalk, LINE, PTE, and node2vec. In: WSDM, pp. 459–467 (2018)
[15]
Sánchez P and Bellogín A Point-of-interest recommender systems based on location-based social networks: a survey from an experimental perspective CSUR 2022 54 1-37
[16]
Song, X., Li, B., Dai, T., Tian, J.: A trust management-based route planning scheme in LBS network. In: ADMA, pp. 307–322 (2022)
[17]
Sun H et al. What your next check-in might look like: next check-in behavior prediction TIST 2023 14 6 1-21
[18]
Sun, X., et al.: Heterogeneous hypergraph embedding for graph classification. In: WSDM, pp. 725–733 (2021)
[19]
Trung HT, Van Vinh T, Tam NT, Jo J, Yin H, and Hung NQV Learning holistic interactions in LBSNs with high-order, dynamic, and multi-role contexts TKDE 2022 35 5 5002-5016
[20]
Tu, K., Cui, P., Wang, X., Wang, F., Zhu, W.: Structural deep embedding for hyper-networks. In: AAAI (2018)
[21]
Wang, Y., Tang, S., Lei, Y., Song, W., Wang, S., Zhang, M.: DisenHAN: disentangled heterogeneous graph attention network for recommendation. In: CIKM, pp. 1605–1614 (2020)
[22]
Wu Z, Pan S, Chen F, Long G, Zhang C, and Philip SY A comprehensive survey on graph neural networks TNNLS 2020 32 1 4-24
[23]
Yang, D., Qu, B., Yang, J., Cudre-Mauroux, P.: Revisiting user mobility and social relationships in LBSNs: a hypergraph embedding approach. In: WWW, pp. 2147–2157 (2019)
[24]
Yang D, Qu B, Yang J, and Cudre-Mauroux P LBSN2Vec++: heterogeneous hypergraph embedding for location-based social networks TKDE 2022 34 4 1843-1855
[25]
Yu, J., Yin, H., Li, J., Wang, Q., Hung, N.Q.V., Zhang, X.: Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: WWW, pp. 413–424 (2021)
[26]
Zhang, S., Li, Z., Wang, X., Chen, Z., Guo, W.: TKGAT: temporal knowledge graph representation learning using attention network. In: ADMA, pp. 46–61 (2023)
[27]
Zong, C., Gong, P., Zhang, X., Qiu, T., Zhang, A., Wang, M.x.: Efficient size-constrained (k, d)-truss community search. In: ADMA, pp. 405–420 (2023)

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Published In

cover image Guide Proceedings
Advanced Data Mining and Applications: 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part III
Dec 2024
464 pages
ISBN:978-981-96-0820-1
DOI:10.1007/978-981-96-0821-8
  • Editors:
  • Quan Z. Sheng,
  • Gill Dobbie,
  • Jing Jiang,
  • Xuyun Zhang,
  • Wei Emma Zhang,
  • Yannis Manolopoulos,
  • Jia Wu,
  • Wathiq Mansoor,
  • Congbo Ma

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 15 December 2024

Author Tags

  1. location-based social networks
  2. heterogeneous hypergraph learning
  3. multi-task learning
  4. graph neural networks

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