Skip to main content

Advertisement

Springer Nature Link
Log in

Learning embeddings of a heterogeneous behavior network for potential behavior prediction

  • Published:
Frontiers of Information Technology & Electronic Engineering Aims and scope Submit manuscript

Abstract

Potential behavior prediction involves understanding the latent human behavior of specific groups, and can assist organizations in making strategic decisions. Progress in information technology has made it possible to acquire more and more data about human behavior. In this paper, we examine behavior data obtained in real-world scenarios as an information network composed of two types of objects (humans and actions) associated with various attributes and three types of relationships (human-human, human-action, and action-action), which we call the heterogeneous behavior network (HBN). To exploit the abundance and heterogeneity of the HBN, we propose a novel network embedding method, human-action-attribute-aware heterogeneous network embedding (a4HNE), which jointly considers structural proximity, attribute resemblance, and heterogeneity fusion. Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.

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

Similar content being viewed by others

References

  • Backstrom L, Leskovec J, 2011. Supervised random walks: predicting and recommending links in social networks. Proc 4th ACM Int Conf on Web Search and Data Mining, p.635–644.

  • Bhagat S, Cormode G, Muthukrishnan S, 2011. Node classification in social networks. In: Aggarwal C (Ed.), Social Network Data Analytics. Springer, Boston, p.115–148. https://doi.org/10.1007/978-1-4419-8462-3_5

    Chapter  Google Scholar 

  • Chang SY, Han W, Tang JL, et al., 2015. Heterogeneous network embedding via deep architectures. Proc 21st ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.119–128. https://doi.org/10.1145/2783258.2783296

  • Chen T, Sun YZ, 2017. Task-guided and path-augmented heterogeneous network embedding for author identification. Proc 10th ACM Int Conf on Web Search and Data Mining, p.295–304. https://doi.org/10.1145/3018661.3018735

  • Chen YX, Wang CG, 2017. HINE: heterogeneous information network embedding. Int Conf on Database Systems for Advanced Applications, p.180–195. https://doi.org/10.1007/978-3-319-55753-3_12

  • Ding CHQ, He XF, Zha HY, et al., 2001. A min-max cut algorithm for graph partitioning and data clustering. Proc IEEE Int Conf on Data Mining, p.107–114.

  • Dong YX, Chawla N, Swami A, 2017. metapath2vec: scalable representation learning for heterogeneous networks. Proc 23rd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.135–144. https://doi.org/10.1145/3097983.3098036

  • Glorot X, Bordes A, Bengio Y, 2011. Deep sparse rectifier neural networks. Proc 14th Int Conf on Artificial Intelligence and Statistics, p.315–323.

  • Grover A, Leskovec J, 2016. node2vec: scalable feature learning for networks. Proc 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.855–864. https://doi.org/10.1145/2939672.2939754

  • Hanley JA, McNeil BJ, 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1): 29–36. https://doi.org/10.1148/radiology.143.1.7063747

    Article  Google Scholar 

  • Harris DM, Harris S, 2010. Digital Design and Computer Architecture. Morgan Kaufmann, Amsterdam.

    Google Scholar 

  • Huang ZP, Mamoulis N, 2017. Heterogeneous information network embedding for meta path based proximity. https://arxiv.org/abs/1701.05291

  • Koren Y, 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. Proc 14th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.426–434. https://doi.org/10.1145/1401890.1401944

  • Le Q, Mikolov T, 2014. Distributed representations of sentences and documents. Proc 31st Int Conf on Machine Learning, p.1188–1196.

  • Lerman K, Intagorn S, Kang JH, et al., 2012. Using proximity to predict activity in social networks. Proc 21st Int Conf on World Wide Web, p.555–556. https://doi.org/10.1145/2187980.2188124

  • Liben-Nowell D, Kleinberg J, 2007. The link-prediction problem for social networks. J Am Soc Inform Sci Technol, 58(7): 1019–1031. https://doi.org/10.1002/asi.20591

    Article  Google Scholar 

  • Ma H, Zhou DY, Liu C, et al., 2011. Recommender systems with social regularization. Proc 4th ACM Int Conf on Web Search and Data Mining, p.287–296. https://doi.org/10.1145/1935826.1935877

  • Mikolov T, Sutskever I, Chen K, et al., 2013a. Distributed representations of words and phrases and their compositionality. Proc 26th Int Conf on Neural Information Processing Systems, p.3111–3119.

  • Mikolov T, Chen K, Corrado G, et al., 2013b. Efficient estimation of word representations in vector space. https://arxiv.org/abs/1301.3781

  • Ou MD, Cui P, Pei J, et al., 2016. Asymmetric transitivity preserving graph embedding. Proc 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.1105–1114. https://doi.org/10.1145/2939672.2939751

  • Pan SR, Wu J, Zhu XQ, et al., 2016. Tri-party deep network representation. Proc 25th Int Joint Conf on Artificial Intelligence, p.1895–1901.

  • Perozzi B, Al-Rfou R, Skiena S, 2014. DeepWalk: online learning of social representations. Proc 20th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.701–710. https://doi.org/10.1145/2623330.2623732

  • Ribeiro LFR, Saverese PHP, Figueiredo DR, 2017. struc2vec: learning node representations from structural identity. Proc 23rd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.385–394. https://doi.org/10.1145/3097983.3098061

  • Sen P, Namata G, Bilgic M, et al., 2008. Collective classification in network data. AI Mag, 29(3): 93–106.

    Article  Google Scholar 

  • Shi C, Hu BB, Zhao WX, et al., 2019. Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng, 31(2): 357–370. https://doi.org/10.1109/TKDE.2018.2833443

    Article  Google Scholar 

  • Stallings J, Vance E, Yang JS, et al., 2013. Determining scientific impact using a collaboration index. PNAS, 110(24): 9680–9685. https://doi.org/10.1073/pnas.1220184110

    Article  MathSciNet  Google Scholar 

  • Sun XF, Guo J, Ding X, et al., 2016. A general framework for content-enhanced network representation learning. https://arxiv.org/abs/1610.02906

  • Sun Y, Han J, Yan X, et al., 2011. PathSim: meta path-based top-k similarity search in heterogeneous information networks. Proc VLDB Endowm, 4(11): 992–1003.

    Google Scholar 

  • Tang J, Zhang J, Yao LM, et al., 2008. ArnetMiner: extraction and mining of academic social networks. Proc 14th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.990–998. https://doi.org/10.1145/1401890.1402008

  • Tang J, Qu M, Wang MZ, et al., 2015a. LINE: large-scale information network embedding. Proc 24th Int Conf on World Wide Web, p.1067–1077. https://doi.org/10.1145/2736277.2741093

  • Tang J, Qu M, Mei QZ, 2015b. PTE: predictive text embedding through large-scale heterogeneous text networks. Proc 21st ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.1165–1174. https://doi.org/10.1145/2783258.2783307

  • Tang L, Liu H, 2009. Relational learning via latent social dimensions. Proc 15th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.817–826. https://doi.org/10.1145/1557019.1557109

  • Tu C, Liu H, Liu Z, et al., 2017a. CANE: context-aware network embedding for relation modeling. Proc 55th Annual Meeting of the Association for Computational Linguistics, p.1722–1731.

  • Tu C, Zhang Z, Liu Z, et al., 2017b. TransNet: translation-based network representation learning for social relation extraction. Proc 26th Int Joint Conf on Artificial Intelligence, p.2864–2870.

  • van der Maaten L, Hinton G, 2008. Visualizing data using t-SNE. J Mach Learn Res, 9: 2579–2605.

    MATH  Google Scholar 

  • Vazquez A, Flammini A, Maritan A, et al., 2003. Global protein function prediction from protein-protein interaction networks. Nat Biotechnol, 21: 697–700. https://doi.org/10.1038/nbt825

    Article  Google Scholar 

  • Yang C, Liu Z, Zhao D, et al., 2015. Network representation learning with rich text information. Proc 24th Int Conf on Artificial Intelligence, p.2111–2117.

  • Yang C, Sun M, Liu Z, et al., 2017. Fast network embedding enhancement via high order proximity approximation. Proc 26th Int Joint Conf on Artificial Intelligence, p.3894–3900.

  • Yin HZ, Hu ZT, Zhou XF, et al., 2016. Discovering interpretable geo-social communities for user behavior prediction. IEEE 32nd Int Conf on Data Engineering, p.942–953. https://doi.org/10.1109/ICDE.2016.7498303

  • Zhang CX, Swami A, Chawla NV, 2018. CARL: content-aware representation learning for heterogeneous networks. https://arxiv.org/abs/1805.04983

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China

    Yue-yang Wang & Yue-ting Zhuang

  2. School of Big Data & Software Engineering, Chongqing University, Chongqing, 401331, China

    Yue-yang Wang

  3. Hikvision Research Institute, Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou, 310051, China

    Wei-hao Jiang & Shi-liang Pu

Authors
  1. Yue-yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei-hao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shi-liang Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yue-ting Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yue-yang Wang.

Additional information

Compliance with ethics guidelines

Yue-yang WANG, Wei-hao JIANG, Shi-liang PU, and Yue-ting ZHUANG declare that they have no conflict of interest.

Project supported by the National Natural Science Foundation of China (Nos. U1509206, 61625107, and U1611461) and the Key Program of Zhejiang Province, China (No. 2015C01027)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Yy., Jiang, Wh., Pu, Sl. et al. Learning embeddings of a heterogeneous behavior network for potential behavior prediction. Front Inform Technol Electron Eng 21, 422–435 (2020). https://doi.org/10.1631/FITEE.1800493

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/FITEE.1800493

Key words

CLC number