Skip to main content

Advertisement

Springer Nature Link
Log in

How the four-nodes motifs work in heterogeneous node representation?

A case study on aminer

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Heterogeneous information networks (HIN), containing different types of entities with various kinds of interaction relations in between, provide richer information than homogeneous networks. Heterogeneous motifs are induced structural subgraph patterns with semantic in HINs. There has been many works using motifs to participate in the representation learning of HINs, but rarely to understand the respective influences of motifs. Due to the rich semantic information contained in heterogeneous motifs, the effects of different structures are inconsistent in network representation. In this paper, we introduce a case study on AMiner dataset, by extracting the heterogeneous motifs with various types of nodes and edges, especially four-node motifs, the relations between those motifs also are explored. During the study process, we first construct a set of motif instances identified by subgraph isomorphism algorithm as a weighted bipartite graph and then use another semantically related node type to extract target node features from pruned adjacency matrix. Next, a series of experiments are designed to evaluate the effect of each motif and the irrelevance of different motifs. Experimental results show that embeddings by our framework achieves excellent results compared with several state-of-the-art alternatives in node classification and clustering tasks.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
.Figure 3
Figure 4
Figure 5
Figure 6
Algorithm 1
Algorithm 2
Figure 7
Figure 8
Algorithm 3
Figure 9
Figure 10
Figure 11

Similar content being viewed by others

Availability of data and material

All of the materials including figures is owned by the authors and no permissions are required. The initial dataset is from the public dataset of website https://www.aminer.cn/aminernetwork.

Notes

  1. The main symbols used in our framework are given in Table 4.

References

  1. Ahmed, N.K., Rossi, R.A., Lee, J.B., Kong, X., Willke, T.L., Zhou, R., Eldardiry, H.: Learning role-based graph embeddings. stat 1050, 7 (2018)

    Google Scholar 

  2. Arora, S.: A survey on graph neural networks for knowledge graph completion. arXiv preprint arXiv:2007.12374 (2020)

  3. Benson, A.R., Gleich, D.F., Leskovec, J.: Higher-order organization of complex networks. Science 353(6295), 163–166 (2016)

    Article  Google Scholar 

  4. Carletti, V., Foggia, P., Saggese, A., Vento, M.: Challenging the time complexity of exact subgraph isomorphism for huge and dense graphs with vf3. IEEE Transactions on Pattern Analysis and Machine Intelligence 40(4), 804–818 (2018). https://doi.org/10.1109/TPAMI.2017.2696940

    Article  Google Scholar 

  5. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)

    Article  MATH  Google Scholar 

  6. Dareddy, M.R., Das, M., Yang, H.: motif2vec: Motif aware node representation learning for heterogeneous networks. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 1052–1059. IEEE (2019). https://doi.org/10.1109/BigData47090.2019.9005670

  7. 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, KDD ’17, p. 135–144. Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3097983.3098036.

  8. Dong, Y., Hu, Z., Wang, K., Sun, Y., Tang, J.: Heterogeneous network representation learning. In: IJCAI 20, 4861–4867 (2020)

    Google Scholar 

  9. Fan, S., Zhu, J., Han, X., Shi, C., Hu, L., Ma, B., Li, Y.: Metapath-guided heterogeneous graph neural network for intent recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2478–2486 (2019)

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

  11. He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322–1328 (2008). https://doi.org/10.1109/IJCNN.2008.4633969

  12. Hosseini, A., Chen, T., Wu, W., Sun, Y., Sarrafzadeh, M.: Heteromed: Heterogeneous information network for medical diagnosis. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 763–772 (2018)

  13. Hou, S., Ye, Y., Song, Y., Abdulhayoglu, M.: Hindroid: An intelligent android malware detection system based on structured heterogeneous information network. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1507–1515 (2017)

  14. Huang, Z., Zheng, Y., Cheng, R., Sun, Y., Mamoulis, N., Li, X.: Meta structure: Computing relevance in large heterogeneous information networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)

  15. Hulovatyy, Y., Chen, H., Milenković, T.: Exploring the structure and function of temporal networks with dynamic graphlets. Bioinformatics 31(12), i171–i180 (2015)

    Article  Google Scholar 

  16. Kovanen, L., Karsai, M., Kaski, K., Kertész, J., Saramäki, J.: Temporal motifs in time-dependent networks. Journal of Statistical Mechanics: Theory and Experiment 2011(11), P11005 (2011)

    Article  Google Scholar 

  17. Lichtenwalter, R.N., Chawla, N.V.: Vertex collocation profiles: subgraph counting for link analysis and prediction. In: Proceedings of the 21st international conference on World Wide Web, pp. 1019–1028 (2012)

  18. Ling, C.X., Li, C.: Data mining for direct marketing: Problems and solutions. In: KDD (1998)

  19. Mahendra Piraveenan Kishan Wimalawarne, D.K.: Centrality and composition of four-node motifs in metabolic networks. Procedia Computer Science 18, 409–418 (2013). https://doi.org/10.1016/j.procs.2013.05.204.  2013 International Conference on Computational Science

  20. Milenković, T., Pržulj, N.: Uncovering biological network function via graphlet degree signatures. Cancer informatics 6, CIN–S680 (2008)

  21. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: Simple building blocks of complex networks. Science 298(5594), 824–827 (2002). https://doi.org/10.1126/science.298.5594.824

    Article  Google Scholar 

  22. Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. nature 435(7043), 814–818 (2005)

  23. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proceedings of the national academy of sciences 101(9), 2658–2663 (2004)

    Article  Google Scholar 

  24. Rossi, R.A., Ahmed, N.K., Carranza, A., Arbour, D., Rao, A., Kim, S., Koh, E.: Heterogeneous network motifs. CoRR abs/1901.10026 (2019). http://arxiv.org/abs/1901.10026

  25. Rossi, R.A., Ahmed, N.K., Carranza, A., Arbour, D., Rao, A., Kim, S., Koh, E.: Heterogeneous graphlets. ACM Transactions on Knowledge Discovery from Data (TKDD) 15(1), 1–43 (2020)

    Article  Google Scholar 

  26. Rossi, R.A., Ahmed, N.K., Koh, E., Kim, S., Rao, A., Yadkori, Y.A.: Hone: Higher-order network embeddings. arXiv preprint arXiv:1801.09303 (2018)

  27. Sankar, A., Zhang, X., Chang, K.C.C.: Meta-gnn: Metagraph neural network for semi-supervised learning in attributed heterogeneous information networks. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’19, p. 137–144. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3341161.3342859

  28. Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., Borgwardt, K.: Efficient graphlet kernels for large graph comparison. In: Artificial intelligence and statistics, pp. 488–495. PMLR (2009)

  29. 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 (2015)

  30. Solava, R.W., Michaels, R.P., Milenković, T.: Graphlet-based edge clustering reveals pathogen-interacting proteins. Bioinformatics 28(18), i480–i486 (2012)

    Article  Google Scholar 

  31. Sorokin, D., Gurevych, I.: Modeling semantics with gated graph neural networks for knowledge base question answering. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 3306–3317. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1280. Accessed  Feb 2022

  32. Vishwanathan, S.V.N., Schraudolph, N.N., Kondor, R., Borgwardt, K.M.: Graph kernels. Journal of Machine Learning Research 11, 1201–1242 (2010)

    MathSciNet  MATH  Google Scholar 

  33. Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., Yu, P.S.: A survey on heterogeneous graph embedding: methods, techniques, applications and sources. arXiv preprint arXiv:2011.14867 (2020)

  34. Xiao, Y., Zhang, J., Deng, L.: Prediction of lncrna-protein interactions using hetesim scores based on heterogeneous networks. Scientific Reports 7(1), 3664 (2017)

    Article  Google Scholar 

  35. Yuan, F., Wenqing, L., W., V., Min, W., Jiaqi, S., Kevin, C., Chen-Chuan, Xiao-Li, L.: Metagraph-based learning on heterogeneous graphs. IEEE Transactions on Knowledge and Data Engineering 33(1), 154–168 (2021). https://doi.org/10.1109/TKDE.2019.2922956

  36. Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330961.

  37. Zhao, J., Wang, X., Shi, C., Liu, Z., Ye, Y.: Network schema preserving heterogeneous information network embedding. In: C. Bessiere (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pp. 1366–1372. International Joint Conferences on Artificial Intelligence Organization (2020). https://doi.org/10.24963/ijcai.2020/190. Main track

  38. Zhengdao, C., Lei, C., Soledad, V., Bruna, J.: Can graph neural networks count substructures? Advances in neural information processing systems (2020). https://par.nsf.gov/biblio/10233869

  39. Zhou, Z.H., Liu, X.Y.: On multi-class cost-sensitive learning. Computational Intelligence 26 (2010)

Download references

Acknowledgements

The authors would like to acknowledge the support provided by the National Natural Science Foundation of China under Grant 61872222, the Key Research and Development Program of Shandong Province (2020CXGC010102), and the project ZR2020LZH011 supported by Shandong Provincial Natural Science Foundation.

Funding

This work is supported by the National Natural Science Foundation of China under Grant 61872222, the Key Research and Development Program of Shandong Province (2020CXGC010102), and the project ZR2020LZH011 supported by Shandong Provincial Natural Science Foundation.

Author information

Authors and Affiliations

  1. School of Software, Shandong University, Jinan, China

    Siyuan Ye, Guangxu Mei, Shijun Liu & Li Pan

  2. School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, Australia

    Qian Li

Authors
  1. Siyuan Ye
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Qian Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Guangxu Mei
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Shijun Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Li Pan
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Siyuan Ye wrote the main manuscript text and prepared all figures and tables. Siyuan Ye, Guangxu Mei and Shijun Liu provided the Methodology. Qian Li, Shijun Liu and Li Pan provided writing-review and editing. Shijun Liu and Li Pan provided funding support.

Corresponding authors

Correspondence to Shijun Liu or Li Pan.

Ethics declarations

Ethical Approval

This declaration is not applicable.

Conflicts of interest

I declare that all authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and discussion reported in this paper.

Additional information

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 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

Ye, S., Li, Q., Mei, G. et al. How the four-nodes motifs work in heterogeneous node representation?. World Wide Web 26, 1707–1729 (2023). https://doi.org/10.1007/s11280-022-01115-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-022-01115-1

Keywords