Skip to main content
SpringerLink
Log in

Global-local neighborhood based network representation for citation recommendation

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Many researchers study citation recommendation approaches using network representation recently. It learns low-dimensional vector representation of nodes in a citation network, generates a recommendation list using similarity scores within the obtained node representations. Most of the existing approaches learn network representation by preserving structure information ofthe citation network. However, nodes in a citation network often associated with content information, recent approaches tend to learn each node’s structure and content representations separately, and apply a simple and empirical combination strategy to produce the final node representation vectors which are suboptimal. To solve the above problems, we propose a Global-Local Neighborhoods based Network Representation model, named GLNNR, to integrate network structure and non- structural information, obtaining better node representation in the network. For global neighborhoods, we first generate parallel sequences containing node identity sequences and the corresponding content sequences, then we propose an attention- based sequence to sequence component to obtain node embeddings (the learned hidden representations of encoder) under global neighborhoods. For local neighborhoods, inspired by matrix factorization, a component can be designed to fuse structural and non-structural information in the lower-order neighborhood, here we first build a co-occurrence matrix (adjacent matrix) of the network, and then use multilayer perceptron to learn node representations under the lower-order neighborhood. The final node representations are global-local neighborhood based node embeddings. Empirical experiments prove the effectiveness of the GLNNR on a real-world information citation networks, i.e. CiteSeer and DBLP. Besides, we conduct experiments on a web page citation network, i.e. Wikipedia, to prove extensibility and portability of the proposed model.

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
Fig. 12

Similar content being viewed by others

Notes

  1. https://s3.us-east-2.amazonaws.com/dgl.ai/dataset/citeseer.zip

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

  3. https://github.com/shenweichen/GraphEmbedding/tree/master/data/wiki

References

  1. Khabsa M, Giles CL (2014) The number of scholarly documents on the public web. PloS one 9(5):e93949

    Article  Google Scholar 

  2. Fu Z, Wu X, Wang Q, Ren K (2017) Enabling central keyword-based semantic extension search over encrypted outsourced data. IEEE Transactions on Information Forensics and Security 12(12):2986–2997

    Article  Google Scholar 

  3. Chen C, Zhu X, Shen P, Hu J, Guo S, Tari Z, Zomaya AY (2015) An efficient privacy-preserving ranked keyword search method. IEEE Transactions on Parallel and Distributed Systems 27(4):951–963

    Article  Google Scholar 

  4. El-Arini K, Guestrin C (2011) Beyond keyword search: discovering relevant scientific literature. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 439–447

  5. Rusiñol M, Aldavert D, Toledo R, Lladós J (2015) Efficient segmentation-free keyword spotting in historical document collections. Pattern recognition 48(2):545–555

    Article  Google Scholar 

  6. Krallinger M, Rabal O, Lourenco A, Oyarzabal J, Valencia A (2017) Information retrieval and text mining technologies for chemistry. Chemical reviews 117(12):7673–7761

    Article  Google Scholar 

  7. Ma L, Song D, Liao L, Wang J (2019) A hybrid discriminative mixture model for cumulative citation recommendation. IEEE Trans Knowl Data Eng 32(4):617–630

    Article  Google Scholar 

  8. Ma X, Zhang Y, Zeng J (2019) Newly published scientific papers recommendation in heterogeneous information networks. Mobile Networks and Applications 24(1):69–79

    Article  Google Scholar 

  9. Habib R, Afzal MT (2019) Sections-based bibliographic coupling for research paper recommendation. Scientometrics 119(2):643–656

    Article  Google Scholar 

  10. Cai X, Han J, Li W, Zhang R, Pan S, Yang L (2018) A three-layered mutually reinforced model for personalized citation recommendation. IEEE transactions on neural networks and learning systems 29(12):6026–6037

    Article  Google Scholar 

  11. Jeong C, Jang S, Shin H, Park E, Choi S (2019) A context-aware citation recommendation model with bert and graph convolutional networks, arXiv preprint arXiv:1903.06464

  12. Mei L, Ren P, Chen Z, Nie L, Ma J, Nie J-Y (2018) An attentive interaction network for context-aware recommendations. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp 157–166

  13. Kong X, Mao M, Wang W, Liu J, Xu B (2018) Voprec: Vector representation learning of papers with text information and structural identity for recommendation IEEE Transactions on emerging topics in computing

  14. Cai X, Han J, Yang L (2018) Generative adversarial network based heterogeneous bibliographic network representation for personalized citation recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

  15. Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 855–864

  16. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, pp 1067–1077

  17. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 701–710

  18. Kruskal JB (1978) Multidimensional scaling, no. 11 Sage

  19. Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  20. Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in neural information processing systems 14:585–591

    Google Scholar 

  21. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  22. Yang C, Liu Z, Zhao D, Sun M, Chang EY (2015) Network representation learning with rich text information.. In: IJCAI, Vol. 2015, pp 2111–2117

  23. Gao M, Chen L, He X, Zhou A (2018) Bine: Bipartite network embedding. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 715–724

  24. Cao S, Lu W, Xu Q (2015) Grarep: Learning graph representations with global structural information. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pp 891–900

  25. Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1105–1114

  26. Meng F, Gao D, Li W, Sun X, Hou Y (2013) A unified graph model for personalized query-oriented reference paper recommendation. In: Proceedings of the 22nd ACM international conference on Information & Knowledge Management, pp 1509–1512

  27. McNee SM, Albert I, Cosley D, Gopalkrishnan P, Lam SK, Rashid AM, Konstan JA, Riedl J (2002) On the recommending of citations for research papers. In: Proceedings of the 2002 ACM conference on computer supported cooperative work, pp 116–125

  28. Yang C, Wei B, Wu J, Zhang Y, Zhang L (2009) Cares: a ranking-oriented cadal recommender system. In: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries, pp 203–212

  29. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web, springer, pp 325–341

  30. Chandrasekaran K, Gauch S, Lakkaraju P, Luong HP (2008) Concept-based document recommendations for citeseer authors. In: International conference on adaptive hypermedia and adaptive web-based systems, springer, pp 83–92

  31. Nascimento C, Laender AH, da Silva AS, Gonçalves M. A. (2011) A source independent framework for research paper recommendation. In: Proceedings of the 11th annual international ACM/IEEE joint conference on digital libraries, pp 297– 306

  32. Hanyurwimfura D, Bo L, Havyarimana V, Njagi D, Kagorora F (2015) An effective academic research papers recommendation for non-profiled users. International Journal of Hybrid Information Technology 8(3):255–272

    Article  Google Scholar 

  33. Cai X, Han J, Pan S, Yang L (2018) Heterogeneous information network embedding based personalized query-focused astronomy reference paper recommendation International Journal of Computational Intelligence Systems

  34. Weinberger K, Dasgupta A, Langford J, Smola A, Attenberg J (2009) Feature hashing for large scale multitask learning. In: Proceedings of the 26th annual international conference on machine learning, pp 1113–1120

  35. Hinton G, Srivastava N, Swersky K, Tieleman T, Mohamed A (2012) Coursera: Neural networks for machine learning Lecture 9c: Using noise as a regularizer

  36. Wang D, Cui P, Zhu W (2018) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1225–1234

  37. Ahmed A, Shervashidze N, Narayanamurthy S, Josifovski V, Smola AJ (2013) Distributed large-scale natural graph factorization. In: Proceedings of the 22nd international conference on World Wide Web, pp 37–48

  38. Golub GH, Van Loan CF (2013) Matrix computations, Vol. 3 JHU press

  39. Kumar R, Verma B, Rastogi SS (2014) Social popularity based svd++ recommender system. International Journal of Computer Applications 87 (14)

  40. Yang C, Liu Z, Zhao D, Sun M, Chang EY (2015) Network representation learning with rich text information.. In: IJCAI, vol 2015, pp 2111–2117

  41. Bandyopadhyay S, Kara H, Kannan A, Murty MN (2018) Fscnmf:, Fusing structure and content via non-negative matrix factorization for embedding information networks, arXiv preprint arXiv:1804.05313

  42. Liu J, He Z, Wei L, Huang Y (2018) Content to node: Self-translation network embedding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1794–1802

  43. Shan Y, Hoens TR, Jiao J, Wang H, Yu D, Mao J (2016) Deep crossing: Web-scale modeling without manually crafted combinatorial features. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 255–262

  44. Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, Vol. 1 MIT press Cambridge

  45. He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5353–5360

  46. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision, Springer, pp 630–645

  47. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization arXiv e-prints

  48. Vinyals O, Kaiser Ł, Koo T, Petrov S, Sutskever I, Hinton G (2015) Grammar as a foreign language. Advances in neural information processing systems 28:2773–2781

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Key Research and Development Project of China (2018YFB1402604), the National Natural Science Foundation of China (Nos.61872296, 61772429, U20B2065), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No.18YJC870001).

Author information

Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Xiaoyan Cai

  2. School of Cyberspace Security, Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Nanxin Wang, Libin Yang & Xin Mei

Authors
  1. Xiaoyan Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nanxin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Libin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Libin Yang.

Additional information

Publisher’s note

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

Xiaoyan Cai and Nanxin Wang contributed equally to this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cai, X., Wang, N., Yang, L. et al. Global-local neighborhood based network representation for citation recommendation. Appl Intell 52, 10098–10115 (2022). https://doi.org/10.1007/s10489-021-02964-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02964-5

Keywords

Search

Navigation