Skip to main content
SpringerLink
Log in

Graph Convolutional Network Using a Reliability-Based Feature Aggregation Mechanism

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12112))

Included in the following conference series:

  • 2776 Accesses

Abstract

Graph convolutional networks (GCNs) have been proven extremely effective in a variety of prediction tasks. The general idea is to update the embedding of a node by recursively aggregating features from the node’s neighborhood. To improve the training efficiency, modern GCNs usually sample a fixed-size set of neighbors uniformly or sample according to nodes’ importance, instead of using the full neighborhood. However, both the sampling strategies ignore the reliability of a link between the target node and its neighbor, which can be implied by the graph structure and may seriously impact the performance of GCNs. To deal with this problem, we present a Graph Convolutional Network using a Reliability-based Feature Aggregation Mechanism called GraphRFA, where we sample the neighbors for each node according to different kinds of link reliability and further aggregate feature information from different reliability-specific neighborhoods by a dual feature aggregation scheme. We also theoretically prove that our aggregation scheme is permutation invariant for the graph data, and provide two simple but effective instantiations satisfying such scheme. Experimental results demonstrate the effectiveness of GraphRFA on different datasets.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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.

    We set \(\lambda =0.5\) in our experiments.

  2. 2.

    http://snap.stanford.edu/graphsage/.

  3. 3.

    https://github.com/williamleif/GraphSAGE.

  4. 4.

    http://networkrepository.com/soc-political-retweet.php.

  5. 5.

    http://snap.stanford.edu/graphsage/.

  6. 6.

    We set the dropping rate to be 0.2 on the Reddit.

References

  1. Ahmed, A., Shervashidze, N., Narayanamurthy, S.M., Josifovski, V., Smola, A.J.: Distributed large-scale natural graph factorization. In: WWW (2013)

    Google Scholar 

  2. Barabási, A., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  3. Chen, H., Yin, H., Chen, T., Nguyen, Q.V.H., Peng, W., Li, X.: Exploiting centrality information with graph convolutions for network representation learning. In: ICDE (2019)

    Google Scholar 

  4. Chen, J., Zhu, J., Song, L.: Stochastic training of graph convolutional networks with variance reduction. In: ICML (2018)

    Google Scholar 

  5. Chen, J., Ma, T., Xiao, C.: FastGCN: fast learning with graph convolutional networks via importance sampling. In: ICLR (2018)

    Google Scholar 

  6. Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833–852 (2019)

    Article  Google Scholar 

  7. Dave, V.S., Zhang, B., Chen, P., Hasan, M.A.: Neural-brane: neural Bayesian personalized ranking for attributed network embedding. Data Sci. Eng. 4(2), 119–131 (2019). https://doi.org/10.1007/s41019-019-0092-x

    Article  Google Scholar 

  8. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: NeurIPS (2016)

    Google Scholar 

  9. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. U.S.A. 99(12), 7821–7826 (2002)

    Article  MathSciNet  Google Scholar 

  10. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD (2016)

    Google Scholar 

  11. Guimerà, R., Sales-Pardo, M.: Missing and spurious interactions and the reconstruction of complex networks. Proc. Nat. Acad. Sci. U.S.A. 106(52), 22073–22078 (2009)

    Article  Google Scholar 

  12. Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: methods and applications. IEEE Data Eng. Bull. 40(3), 52–74 (2017)

    Google Scholar 

  13. Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NeurIPS (2017)

    Google Scholar 

  14. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953). https://doi.org/10.1007/BF0228902610.1007/BF02289010.1007/BF02289026

    Article  MATH  Google Scholar 

  15. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)

    Google Scholar 

  16. Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)

    Article  Google Scholar 

  17. Liu, W., Lü, L.: Link prediction based on local random walk. EPL (Europhys. Lett.) 89(5), 58007 (2010)

    Article  Google Scholar 

  18. Liu, Z., et al.: GeniePath: graph neural networks with adaptive receptive paths. In: AAAI (2019)

    Google Scholar 

  19. Lü, L., Jin, C., Zhou, T.: Similarity index based on local paths for link prediction of complex networks. Phys. Rev. E 80(4 Pt 2), 046122 (2009)

    Article  Google Scholar 

  20. Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A 390(6), 1150–1170 (2011)

    Article  Google Scholar 

  21. Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: KDD (2014)

    Google Scholar 

  22. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)

    Google Scholar 

  23. Weisfeiler, B., Lehman, A.: A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsia 2(9), 12–16 (1968)

    Google Scholar 

  24. Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: ICLR (2019)

    Google Scholar 

  25. Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: KDD (2018)

    Google Scholar 

  26. Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W.L., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: NeurIPS (2018)

    Google Scholar 

  27. Yun, S., Jeong, M., Kim, R., Kang, J., Kim, H.J.: Graph transformer networks. In: NeurIPS (2019)

    Google Scholar 

  28. Zeng, A., Cimini, G.: Removing spurious interactions in complex networks. Phys. Rev. E 85(3 Pt 2), 036101 (2012)

    Article  Google Scholar 

  29. Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: NeurIPS (2018)

    Google Scholar 

  30. Zhou, T., Lü, L., Zhang, Y.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by National Key R & D Program of China (No.2018YFB1004401) and NSFC under the grant No. 61532021, 61772537, 61772536, 61702522.

Author information

Authors and Affiliations

  1. Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China, Beijing, China

    Yanling Wang, Cuiping Li, Jing Zhang, Peng Ni & Hong Chen

  2. School of Information, Renmin University of China, Beijing, China

    Yanling Wang, Cuiping Li, Jing Zhang, Peng Ni & Hong Chen

Authors
  1. Yanling Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cuiping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cuiping Li .

Editor information

Editors and Affiliations

  1. Dankook University, Yongin, Korea (Republic of)

    Yunmook Nah

  2. Peking University, Haidian, China

    Bin Cui

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Sang-Won Lee

  4. Department of System Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, Hong Kong

    Jeffrey Xu Yu

  5. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Steven Euijong Whang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Y., Li, C., Zhang, J., Ni, P., Chen, H. (2020). Graph Convolutional Network Using a Reliability-Based Feature Aggregation Mechanism. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_36

Download citation

Publish with us

Policies and ethics

Search

Navigation