Skip to main content
SpringerLink
Log in

Structure-Preserved Heterogeneous Graph Representation

  • Chapter
  • First Online:
Heterogeneous Graph Representation Learning and Applications

  • 1310 Accesses

Abstract

Heterogeneous graph (HG) contains various types of nodes or links, which are highly correlated and present intricate structures due to different links. These structures reflect the crucial factors of topology. Therefore encoding meaningful structures is a basic requirement to obtain node representations with high quality. So far, some representative structures have been studied in an HG, from one-hop edges to high-order local structures, such as meta-paths and network schema. In this chapter, we will introduce several works focusing on structure preservation. By capturing respective structures, they successfully depict the rich semantics and complex heterogeneity, and effectively support downstream tasks.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://movie.douban.com.

  2. 2.

    http://book.douban.com.

  3. 3.

    http://www.yelp.com/dataset-challenge.

  4. 4.

    https://dblp.uni-trier.de.

  5. 5.

    https://www.yelp.com/dataset/.

  6. 6.

    https://www.aminer.cn/citation.

  7. 7.

    https://github.com/Andy-Border/NSHE.

References

  1. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Preprint. arXiv:1409.0473 (2014)

    Google Scholar 

  2. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)

    Google Scholar 

  3. Cao, S., Lu, W., Xu, Q.: GraRep: Learning graph representations with global structural information. In: CIKM ’15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 891–900 (2015)

    Google Scholar 

  4. Chang, S., Han, W., Tang, J., Qi, G.-J., Aggarwal, C.C., Huang, T.S.: Heterogeneous network embedding via deep architectures. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 119–128. ACM, New York (2015)

    Google Scholar 

  5. Danielsson, P.-E.: Euclidean distance mapping. Comput. Graphics Image Process. 14(3), 227–248 (1980)

    Article  Google Scholar 

  6. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. Preprint. arXiv:1810.04805 (2018)

    Google Scholar 

  7. Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: Scalable representation learning for heterogeneous networks. In: KDD ’17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144 (2017)

    Google Scholar 

  8. Faust, K.: Centrality in affiliation networks. Soc. Netw. 19(2), 157–191 (1997)

    Article  Google Scholar 

  9. Fu, T.-y., Lee, W.-C., Lei, Z.: Hin2vec: explore meta-paths in heterogeneous information networks for representation learning. In: CIKM ’17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1797–1806 (2017)

    Google Scholar 

  10. 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. ACM, New York (2016)

    Google Scholar 

  11. Han, X., Shi, C., Wang, S., Philip, S.Y., Song, L.: Aspect-level deep collaborative filtering via heterogeneous information networks. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 3393–3399 (2018)

    Google Scholar 

  12. Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Inter. Intell. Syst. 5(4), 19 (2016)

    Google Scholar 

  13. He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: WWW ’16: Proceedings of the 25th International Conference on World Wide Web, pp. 507–517 (2016)

    Google Scholar 

  14. He, X., Zhang, H., Kan, M.-Y., Chua, T.-S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 549–558 (2016)

    Google Scholar 

  15. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: WWW ’17: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)

    Google Scholar 

  16. Hsieh, C.-K., Yang, L., Cui, Y., Lin, T.-Y., Belongie, S., Estrin, D.: Collaborative metric learning. In: WWW ’17: Proceedings of the 26th International Conference on World Wide Web, pp. 193–201 (2017)

    Google Scholar 

  17. Hu, B., Fang, Y., Shi, C.: Adversarial learning on heterogeneous information networks. In: KDD ’19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 120–129 (2019)

    Google Scholar 

  18. Ji, M., Han, J., Danilevsky, M.: Ranking-based classification of heterogeneous information networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1298–1306 (2011)

    Google Scholar 

  19. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference for Learning Representations (2015)

    Google Scholar 

  20. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations (2017)

    Google Scholar 

  21. Koren, Y., Bell, R., and Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 8 (2009)

    Article  Google Scholar 

  22. Linmei, H., Yang, T., Shi, C., Ji, H., Li, X.: Heterogeneous graph attention networks for semi-supervised short text classification. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4823–4832 (2019)

    Google Scholar 

  23. Lu, Y., Shi, C., Hu, L., Liu, Z.: Relation structure-aware heterogeneous information network embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4456–4463 (2019)

    Google Scholar 

  24. Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296 (2011)

    Google Scholar 

  25. McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52 (2015)

    Google Scholar 

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

    Google Scholar 

  27. Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. Adv. Neural Inf. Proces. Syst. 20, 1257–1264 (2007)

    Google Scholar 

  28. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: KDD ’14: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)

    Google Scholar 

  29. Pham, T.-A.N., Li, X., Cong, G., Zhang, Z.: A general recommendation model for heterogeneous networks. IEEE Trans. Knowl. Data Eng. 28(12), 3140–3153 (2016)

    Article  Google Scholar 

  30. Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(3), 1–22 (2012)

    Article  Google Scholar 

  31. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: UAI ’09: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)

    Google Scholar 

  32. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)

    Google Scholar 

  33. Shang, J., Qu, M., Liu, J., Kaplan, L.M., Han, J., Peng, J.: Meta-path guided embedding for similarity search in large-scale heterogeneous information networks. Preprint. arXiv:1610.09769 (2016)

    Google Scholar 

  34. Shi, C., Kong, X., Huang, Y., Philip, S.Y., Wu, B.: HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479–2492 (2014)

    Article  Google Scholar 

  35. Shi, C., Zhang, Z., Luo, P., Yu, P.S., Yue, Y., Wu, B.: Semantic path based personalized recommendation on weighted heterogeneous information networks. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 453–462. ACM, New York (2015)

    Google Scholar 

  36. Shi, C., Han, X., Li, S., Wang, X., Wang, S., Du, J., Yu, P.: Deep collaborative filtering with multi-aspect information in heterogeneous networks. IEEE Trans. Knowl. Data Eng. (2019)

    Google Scholar 

  37. Shi, C., Hu, B., Zhao, W.X., Yu, P.S.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357–370 (2019)

    Article  Google Scholar 

  38. Sun, Y., Yu, Y., Han, J.: Ranking-based clustering of heterogeneous information networks with star network schema. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 797–806 (2009)

    Google Scholar 

  39. 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)

    Article  Google Scholar 

  40. 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)

    Article  Google Scholar 

  41. Sun, Y., Norick, B., Han, J., Yan, X., Philip, S.Y., Yu, X.: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. In: ACM Transactions on Knowledge Discovery from Data (2012)

    Google Scholar 

  42. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998. ACM, New York (2008)

    Google Scholar 

  43. Tang, J., Qu, M., Mei, Q.: PTE: predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1165–1174. ACM, New York (2015)

    Google Scholar 

  44. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)

    Google Scholar 

  45. Tu, K., Cui, P., Wang, X., Wang, F., Zhu, W.: Structural deep embedding for hyper-networks. In: Thirty-Second AAAI Conference on Artificial Intelligence, pp. 426–433 (2018)

    Google Scholar 

  46. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  47. Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  48. Wang, H., Zhang, F., Hou, M., Xie, X., Guo, M., Liu, Q.: SHINE: signed heterogeneous information network embedding for sentiment link prediction. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 592–600. ACM, New York (2018)

    Google Scholar 

  49. Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., Yu, P.S.: Heterogeneous graph attention network. In: The World Wide Web Conference, pp. 2022–2032 (2019)

    Google Scholar 

  50. Wang, Y., Chen, L., Che, Y., Luo, Q.: Accelerating pairwise SimRank estimation over static and dynamic graphs. VLDB J. Int. J. Very Large Data Bases 28(1), 99–122 (2019)

    Article  Google Scholar 

  51. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press, Cambridge (1994)

    Book  Google Scholar 

  52. Wei, Z., He, X., Xiao, X., Wang, S., Liu, Y., Du, X., Wen, J.-R.: PRsim: sublinear time SimRank computation on large power-law graphs. Preprint. arXiv:1905.02354 (2019)

    Google Scholar 

  53. Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y.: Show, attend and tell: neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048–2057 (2015)

    Google Scholar 

  54. Xu, L., Wei, X., Cao, J., Yu, P.S.: Embedding of embedding (EOE): joint embedding for coupled heterogeneous networks. In: WSDM ’17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 741–749. ACM, New York (2017)

    Google Scholar 

  55. Xue, H., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 3203–3209 (2017)

    Google Scholar 

  56. Yang, J., Leskovec, J.: Community-affiliation graph model for overlapping network community detection. In: 2012 IEEE 12th International Conference on Data Mining, pp. 1170–1175. IEEE, Piscataway (2012)

    Google Scholar 

  57. You, Q., Jin, H., Wang, Z., Fang, C., Luo, J.: Image captioning with semantic attention. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4651–4659 (2016)

    Google Scholar 

  58. Yu, X., Ren, X., Gu, Q., Sun, Y., Han, J.: Collaborative filtering with entity similarity regularization in heterogeneous information networks. IJCAI HINA 27 (2013)

    Google Scholar 

  59. Zhang, J., Tang, J., Ma, C., Tong, H., Jing, Y., Li, J.: Panther: fast top-k similarity search on large networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1445–1454 (2015)

    Google Scholar 

  60. Zhang, J., Xia, C., Zhang, C., Cui, L., Fu, Y., Philip, S. Y.: BL-MNE: Emerging heterogeneous social network embedding through broad learning with aligned autoencoder. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 605–614. IEEE, Piscataway (2017)

    Google Scholar 

  61. Zhao, H., Yao, Q., Li, J., Song, Y., Lee, D.: Meta-graph based recommendation fusion over heterogeneous information networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 635–644 (2017)

    Google Scholar 

  62. Zhao, J., Wang, X., Shi, C., Liu, Z., Ye, Y.: Network schema preserving heterogeneous information network embedding. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 1366–1372. IJCAI, ijcai.org (2020)

    Google Scholar 

  63. Zheng, J., Liu, J., Shi, C., Zhuang, F., Li, J., Wu, B.: Recommendation in heterogeneous information network via dual similarity regularization. Int. J. Data Sci. Anal. 3(1), 35–48 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

    Chuan Shi & Xiao Wang

  2. Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

Authors
  1. Chuan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Philip S. Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Shi, C., Wang, X., Yu, P.S. (2022). Structure-Preserved Heterogeneous Graph Representation. In: Heterogeneous Graph Representation Learning and Applications. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-16-6166-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-6166-2_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-6165-5

  • Online ISBN: 978-981-16-6166-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation