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Deepwalk-aware graph convolutional networks

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Abstract

Graph convolutional networks (GCNs) provide a promising way to extract the useful information from graph-structured data. Most of the existing GCNs methods usually focus on local neighborhood information based on specific convolution operations, and ignore the global structure of the input data. To extract the latent representation for the graph-structured data more effectively, we introduce a deepwalk strategy into GCNs to efficiently explore the global graph information. This strategy can complement the local neighborhood information of a graph, resulting in the more robust representation for the graph data. The fusion of the local neighboring and global structured information of a graph can further facilitate deep feature learning at the output layer of GCNs for node classification. Experimental results show that the proposed model has achieved state-of-the-art results on three benchmark datasets including Cora, Citeseer, and Pubmed citation networks.

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References

  1. Gilmer J, Schoenholz S S, Riley P F, et al. Neural message passing for quantum chemistry. In: Proceedings of International Conference on Machine Learning, 2017. 1263–1272

  2. Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009. 248–255

  3. Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of Annual Conference on Neural Information Processing Systems, 2015. 91–99

  4. Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: Proceedings of International Conference of Legal Regulators, 2015

  5. Hammond D K, Vandergheynst P, Gribonval R. Wavelets on graphs via spectral graph theory. Appl Comput Harmonic Anal, 2011, 30: 129–150

    Article  MathSciNet  MATH  Google Scholar 

  6. Bruna J, Zaremba W, Szlam A, et al. Spectral networks and locally connected networks on graphs. In: Proceedings of International Conference of Legal Regulators, 2014

  7. Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of Annual Conference on Neural Information Processing Systems, 2016. 3844–3852

  8. Kipf T, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of International Conference of Legal Regulators, 2017

  9. Klicpera J, Weißenberger S, Günnemann S. Diffusion improves graph learning. In: Proceedings of Annual Conference on Neural Information Processing Systems, 2019. 13333–13345

  10. Velickovic P, Cucurull G, Casanova A, et al. Graph attention networks. In: Proceedings of International Conference of Legal Regulators, 2018

  11. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Proceedings of Annual Conference on Neural Information Processing Systems, 2017. 5998–6008

  12. Monti F, Boscaini D, Masci J, et al. Geometric deep learning on graphs and manifolds using mixture model CNNs. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017. 5115–5124

  13. Chiang W L, Liu X, Si S, et al. Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

  14. Zhuang C, Ma Q. Dual graph convolutional networks for graph-based semi-supervised classification. In: Proceedings of the World Wide Web Conference, 2018. 499–508

  15. Li Q, Han Z, Wu X M. Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018. 3538–3545

  16. Perozzi B, Al-Rfou R, Skiena S. DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014. 701–710

  17. Grover A, Leskovec J. Node2vec: scalable feature learning for networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016

  18. Tang J, Qu M, Wang M, et al. LINE: large-scale information network embedding. In: Proceedings of the World Wide Web Conference, 2015

  19. Wang D, Cui P, Zhu W. Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

  20. Ribeiro L F R, Saverese P H P, Figueiredo D R. Struc2vec: learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017

  21. Perozzi B, Kulkarni V, Chen H, et al. Don’t walk, skip!: online learning of multi-scale network embeddings. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017. 258–265

  22. Cao S, Lu W, Xu Q. Deep neural networks for learning graph representations. In: Proceedings of the Association for the Advance of Artificial Intelligence, 2016

  23. Ru L, Du B, Wu C. Multi-temporal scene classification and scene change detection with correlation based fusion. IEEE Trans Image Process, 2021, 30: 1382–1394

    Article  MathSciNet  Google Scholar 

  24. Zhu D, Du B, Zhang L. Two-stream convolutional networks for hyperspectral target detection. IEEE Trans Geosci Remote Sens, 2021, 59: 6907–6921

    Article  Google Scholar 

  25. Xu Y, Du B, Zhang L. Beyond the patchwise classification: spectral-spatial fully convolutional networks for hyperspectral image classification. IEEE Trans Big Data, 2020, 6: 492–506

    Article  Google Scholar 

  26. Zhou Q, Yang W, Gao G, et al. Multi-scale deep context convolutional neural networks for semantic segmentation. World Wide Web, 2019, 22: 555–570

    Article  Google Scholar 

  27. Zhou Q, Wang Y, Liu J, et al. An open-source project for real-time image semantic segmentation. Sci China Inf Sci, 2019, 62: 227101

    Article  Google Scholar 

  28. Nie W Z, Ren M J, Liu A A, et al. M-GCN: multi-branch graph convolution network for 2D image-based on 3D model retrieval. IEEE Trans Multimedia, 2021, 23: 1962–1976

    Article  Google Scholar 

  29. Zhu J, Yang H, Lin W, et al. Group re-identification with group context graph neural networks. IEEE Trans Multimedia, 2021, 23: 2614–2626

    Article  Google Scholar 

  30. Wang W, Gao J, Yang X, et al. Learning coarse-to-fine graph neural networks for video-text retrieval. IEEE Trans Multimedia, 2021, 23: 2386–2397

    Article  Google Scholar 

  31. Mithun N C, Li J, Metze F, et al. Learning joint embedding with multimodal cues for cross-modal video-text retrieval. In: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 2018. 19–27

  32. Yuan Y, Xiong Z, Wang Q. ACM: adaptive cross-modal graph convolutional neural networks for RGB-D scene recognition. In: Proceedings of the Association for the Advance of Artificial Intelligence, 2019. 9176–9184

  33. Qian X, Zhuang Y, Li Y, et al. Video relation detection with spatio-temporal graph. In: Proceedings of the 27th ACM International Conference on Multimedia, 2019. 84–93

  34. Hamilton W L, Ying Z, Leskovec J. Inductive representation learning on large graphs. In: Proceedings of the Annual Conference on Neural Information Processing Systems, 2017. 1024–1034

  35. Zhang J, Shi X, Xie J, et al. GaAN: gated attention networks for learning on large and spatiotemporal graphs. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2018

  36. Peng Z, Huang W, Luo M, et al. Graph representation learning via graphical mutual information maximization. In: Proceedings of the Web Conference, 2020. 259–270

  37. Abu-El-Haija S, Kapoor A, Perozzi B, et al. N-GCN: multi-scale graph convolution for semi-supervised node classification. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2019. 841–851

  38. Niepert M, Ahmed M O, Kutzkov K. Learning convolutional neural networks for graphs. In: Proceedings of International Conference on Machine Learning, 2016. 2014–2023

  39. Gao H, Wang Z, Ji S. Large-scale learnable graph convolutional networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018. 1416–1424

  40. Wu J, Zhong S H, Liu Y. MvsGCN: a novel graph convolutional network for multi-video summarization. In: Proceedings of the 27th ACM International Conference on Multimedia, 2019. 827–835

  41. Chen J, Ma T, Xiao C. FastGCN: fast learning with graph convolutional networks via importance sampling. In: Proceedings of the International Conference of Legal Regulators, 2018

  42. Huang W, Zhang T, Rong Y, et al. Adaptive sampling towards fast graph representation learning. In: Proceedings of Annual Conference on Neural Information Processing Systems, 2018. 4558–4567

  43. Wei Y, Wang X, Nie L, et al. MMGCN: multi-modal graph convolution network for personalized recommendation of microvideo. In: Proceedings of the 27th ACM International Conference on Multimedia, 2019. 1437–1445

  44. Andersen R, Chung F, Lang K. Local graph partitioning using pagerank vectors. In: Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS’06), 2006. 475–486

  45. Fouss F, Pirotte A, Renders J, et al. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans Knowl Data Eng, 2007, 19: 355–369

    Article  Google Scholar 

  46. Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space. In: Proceedings of ICLR Workshop, 2013

  47. Kingma D P, Ba J. Adam: a method for stochastic optimization. In: Proceedings of the International Conference of Legal Regulators, 2015

  48. Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res, 2014, 15: 1929–1958

    MathSciNet  MATH  Google Scholar 

  49. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. J Mach Learn Res, 2010, 9: 249–256

    Google Scholar 

  50. Chen J, Zhu J, Song L. Stochastic training of graph convolutional networks with variance reduction. In: Proceedings of the International Conference on Machine Learning, 2018

Download references

Acknowledgements

The work was supported by National Key Research and Development Plan Project (Grant Nos. 2018YFC0-830105, 2018YFC0830100), in part by National Science Fund for Distinguished Young Scholars (Grant No. 62025603), in part by National Natural Science Foundation of China (Grant Nos. U1705262, 62072386, 62072387, 62076016, 61772443).

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Authors and Affiliations

  1. Media Analytics and Computing Lab, Department of Computer Science and Technology, School of Informatics, Xiamen University, Xiamen, 361005, China

    Taisong Jin & Liujuan Cao

  2. Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, 361005, China

    Huaqiang Dai & Rongrong Ji

  3. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100083, China

    Baochang Zhang

  4. Tencent Youtu Lab, Shanghai, 200233, China

    Feiyue Huang

  5. School of Software, Tsinghua University, Beijing, 100084, China

    Yue Gao

Authors
  1. Taisong Jin
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  2. Huaqiang Dai
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  3. Liujuan Cao
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  4. Baochang Zhang
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  5. Feiyue Huang
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  6. Yue Gao
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  7. Rongrong Ji
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Correspondence to Liujuan Cao.

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Jin, T., Dai, H., Cao, L. et al. Deepwalk-aware graph convolutional networks. Sci. China Inf. Sci. 65, 152104 (2022). https://doi.org/10.1007/s11432-020-3318-5

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  • DOI: https://doi.org/10.1007/s11432-020-3318-5

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