PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning | International Journal of Machine Learning and Cybernetics Skip to main content
Springer Nature Link
Log in

PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Graph is a widely existed data structure in many real world scenarios, such as social networks, citation networks and knowledge graphs. Recently, Graph Convolutional Network (GCN) has been proposed as a powerful method for graph-based semi-supervised learning, which has the similar operation and structure as Convolutional Neural Networks (CNNs). However, like many CNNs, it is often necessary to go through a lot of laborious experiments to determine the appropriate network structure and parameter settings. Fully exploiting and utilizing the prior knowledge that nearby nodes have the same labels in graph-based neural network is still a challenge. In this paper, we propose a model which utilizes the prior knowledge on graph to enhance GCN. To be specific, we decompose the objective function of semi-supervised learning on graphs into a supervised term and an unsupervised term. For the unsupervised term, we present the concept of local inconsistency and devise a loss term to describe the property in graphs. The supervised term captures the information from the labeled data while the proposed unsupervised term captures the relationships among both labeled data and unlabeled data. Combining supervised term and unsupervised term, our proposed model includes more intrinsic properties of graph-structured data and improves the GCN model with no increase in time complexity. Experiments on three node classification benchmarks show that our proposed model is superior to GCN and seven existing graph-based semi-supervised learning methods.

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
¥17,985 /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 includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Atwood J, Towsley DF (2016) Diffusion-convolutional neural networks. In: Advances in neural information processing systems, pp 1993–2001

  2. Belkin M, Niyogi P, Sindhwani V, Bartlett P (2006) Manifold regularization: a geometric framework for learning from examples. J Mach Learn Res 7(1):2399–2434

    MathSciNet  MATH  Google Scholar 

  3. Blum A, Mitchell TM (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th annual conference on computational learning theory, pp 92–100

  4. Bronstein MM, Bruna J, Lecun Y, Szlam A, Vandergheynst P (2017) Geometric deep learning: going beyond euclidean data. IEEE Signal Process Mag 34(4):18–42

    Article  Google Scholar 

  5. Bruna J, Zaremba W, Szlam A, Lecun Y (2014) Spectral networks and locally connected networks on graphs. In: Proceedings of the 2th international conference on learning representations

  6. Chapelle OZA, Scholkopf B (2006) Semi-supervised learning. MIT Press, Cambridge

    Book  Google Scholar 

  7. Chen J, Ma T, Xiao C (2018) Fastgcn: Fast learning with graph convolutional networks via importance sampling. In: Proceedings of the 6th international conference on learning representations

  8. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. pp 3844–3852

  9. Duvenaud DK, Maclaurin D, Aguileraiparraguirre J, Gomezbombarelli R, Hirzel TD, Aspuruguzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Advances in neural information processing systems, pp 2224–2232

  10. Fout A, Byrd J, Shariat B, Benhur A (2017) Protein interface prediction using graph convolutional networks. In: Advances in neural information processing systems, pp 6530–6539

  11. Fujino A, Ueda N, Saito K (2005) A hybrid generative/discriminative approach to semi-supervised classifier design. In: Proceedings of the 19th AAAI conference on artificial intelligence, pp 764–769

  12. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics, pp 249–256

  13. Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowl Based Syst 151:78–94

    Article  Google Scholar 

  14. 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

  15. Hamilton WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1024–1034

  16. Joachims T (1999) Transductive inference for text classification using support vector machines. In: Proceedings of the 16th international conference on machine learning, pp 200–209

  17. Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the conference on empirical methods in natural language processing, pp 1746–1751

  18. Kingma D, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3th international conference on learning representations

  19. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th international conference on learning representations, pp 1–14

  20. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  21. Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86

    Article  MathSciNet  Google Scholar 

  22. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  23. Li Y, Tarlow D, Brockschmidt M, Zemel RS (2016) Gated graph sequence neural networks. In: Proceedings of the 5th international conference on learning representations

  24. Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: Proceedings of the 27th international joint conference on artificial intelligence

  25. Lin J (1991) Divergence measures based on the shannon entropy. IEEE Trans Inf Theory 37(1):145–151

    Article  MathSciNet  Google Scholar 

  26. Lu Q, Getoor L (2003) Link-based classification. In: Proceedings of the 20th international conference on machine learning, pp 496–503

  27. Mallat S (1999) A wavelet tour of signal processing, 2nd edn. Elsevier, San Diego

    MATH  Google Scholar 

  28. Mcpherson M, Smithlovin L, Cook JM (2001) Birds of a feather: Homophily in social networks. Rev Soc 27(1):415–444

    Google Scholar 

  29. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: Proceedings of the neural information processing systems conference, pp 3111–3119

  30. Monti F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein MM (2017) Geometric deep learning on graphs and manifolds using mixture model cnns. pp 5425–5434

  31. Niepert M, Ahmed MO, Kutzkov K (2016) Learning convolutional neural networks for graphs. In: Proceedings of the 20th international conference on machine learning, pp 2014–2023

  32. Nigam K, Mccallum A, Thrun S, Mitchell TM (2000) Text classification from labeled and unlabeled documents using em. Mach Learn 39(2):103–134

    Article  Google Scholar 

  33. 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

  34. Sainath TN, Vinyals O, Senior A, Sak H (2015) Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 4580–4584

  35. Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80

    Article  Google Scholar 

  36. Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P (2013) The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag 30(3):83–98

    Article  Google Scholar 

  37. Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. In: Proceedings of the 6th international conference on learning representations

  38. Weston J, Ratle F, Mobahi H, Collobert R (2008) Deep learning via semi-supervised embedding. In: Proceedings of the 25th international conference on machine learning, pp 1168–1175

  39. Yang Z, Cohen W, Salakhutdinov R (2016) Revisiting semi-supervised learning with graph embeddings. In: Proceedings of the 33th international conference on machine learning, pp 40–48

  40. Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. pp 3634–3640

  41. Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33(4):452–473

    Article  Google Scholar 

  42. Zhou D, Bousquet O, Lal TN, Weston J, Olkopf BS (2004) Learning with local and global consistency. Adv Neural Inf Process Syst 16:321–328

    Google Scholar 

  43. Zhu X (2005) Zhu X (2005) Semi-supervised learning literature survey. Tech. rep., University of Wisconsin-Madison Department of Computer Sciences

  44. Zhu X, Ghahramani Z, Lafferty JD (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the 20th international conference on machine learning, pp 912–919

Download references

Acknowledgements

This work was funded by the National Natural Science Foundation of China under Grant No. U1636220 and 61532006, and Beijing Municipal Natural Science Foundation under Grant No. 4172063.

Author information

Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, 100190, Beijing, China

    Shaowei Yu, Xuebing Yang & Wensheng Zhang

  2. University of Chinese Academy of Sciences, Beijing, China

    Shaowei Yu

Authors
  1. Shaowei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuebing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wensheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaowei Yu.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, S., Yang, X. & Zhang, W. PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning. Int. J. Mach. Learn. & Cyber. 10, 3115–3127 (2019). https://doi.org/10.1007/s13042-019-01003-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-019-01003-7

Keywords

Search

Navigation