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Adaptive Graph Auto-Encoder for General Data Clustering (2002.08648v6)

Published 20 Feb 2020 in cs.LG and stat.ML

Abstract: Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. However, in general clustering tasks, the graph structure of data does not exist such that the strategy to construct a graph is crucial for performance. Therefore, how to extend graph convolution networks into general clustering tasks is an attractive problem. In this paper, we propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-Euclidean structure sufficiently. We further design a novel mechanism with rigorous analysis to avoid the collapse caused by the adaptive construction. Via combining the generative model for network embedding and graph-based clustering, a graph auto-encoder with a novel decoder is developed such that it performs well in weighted graph used scenarios. Extensive experiments prove the superiority of our model.

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References (38)
  1. J. C. Dunn, “A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters,” Journal of Cybernetics, vol. 3, no. 3, pp. 32–57, 1973.
  2. J. Bezdek, “A convergence theorem for the fuzzy isodata clustering algorithms,” IEEE transactions on Pattern Analysis and Machine Intelligence, no. 1, pp. 1–8, 1980.
  3. R. Zhang, H. Zhang, and X. Li, “Maximum joint probability with multiple representations for clustering,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–11, 2021.
  4. A. Y. Ng, M. I. Jordan, and Y. Weiss, “On spectral clustering: Analysis and an algorithm,” in Advances in neural information processing systems, 2002, pp. 849–856.
  5. L. Hagen and A. B. Kahng, “New spectral methods for ratio cut partitioning and clustering,” IEEE transactions on computer-aided design of integrated circuits and systems, vol. 11, no. 9, pp. 1074–1085, 1992.
  6. F. Nie, X. Wang, and H. Huang, “Clustering and projected clustering with adaptive neighbors,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining.   ACM, 2014, pp. 977–986.
  7. U. Shaham, K. Stanton, H. Li, B. Nadler, R. Basri, and Y. Kluger, “Spectralnet: Spectral clustering using deep neural networks,” arXiv preprint arXiv:1801.01587, 2018.
  8. J. Xie, R. Girshick, and A. Farhadi, “Unsupervised deep embedding for clustering analysis,” in International conference on machine learning, 2016, pp. 478–487.
  9. R. Zhang, X. Li, H. Zhang, and F. Nie, “Deep fuzzy k-means with adaptive loss and entropy regularization,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 11, pp. 2814–2824, 2020.
  10. J. Yang, D. Parikh, and D. Batra, “Joint unsupervised learning of deep representations and image clusters,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 5147–5156.
  11. K. Ghasedi Dizaji, A. Herandi, C. Deng, W. Cai, and H. Huang, “Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 5736–5745.
  12. X. Yang, C. Deng, F. Zheng, J. Yan, and W. Liu, “Deep spectral clustering using dual autoencoder network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 4066–4075.
  13. H. Wang, J. Wang, J. Wang, M. Zhao, W. Zhang, F. Zhang, X. Xie, and M. Guo, “Graphgan: Graph representation learning with generative adversarial nets,” in Thirty-Second AAAI Conference on Artificial Intelligence, 2018, pp. 2508–2515.
  14. B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining.   ACM, 2014, pp. 701–710.
  15. S. Cao, W. Lu, and Q. Xu, “Deep neural networks for learning graph representations,” in Thirtieth AAAI conference on artificial intelligence, 2016.
  16. D. Wang, P. Cui, and W. Zhu, “Structural deep network embedding,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 2016, pp. 1225–1234.
  17. F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Transactions on Neural Networks, vol. 20, no. 1, pp. 61–80, 2008.
  18. T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR, 2017.
  19. M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in neural information processing systems, 2016, pp. 3844–3852.
  20. T. N. Kipf and M. Welling, “Variational graph auto-encoders,” arXiv preprint arXiv:1611.07308, 2016.
  21. C. Wang, S. Pan, G. Long, X. Zhu, and J. Jiang, “Mgae: Marginalized graph autoencoder for graph clustering,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp. 889–898.
  22. C. Wang, S. Pan, R. Hu, G. Long, J. Jiang, and C. Zhang, “Attributed graph clustering: a deep attentional embedding approach,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence.   AAAI Press, 2019, pp. 3670–3676.
  23. G. Hinton and R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006.
  24. M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in International conference on machine learning, 2016, pp. 2014–2023.
  25. J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, “Spectral networks and locally connected networks on graphs,” arXiv preprint arXiv:1312.6203, 2013.
  26. S. Abu-El-Haija, B. Perozzi, A. Kapoor, N. Alipourfard, K. Lerman, H. Harutyunyan, G. Ver Steeg, and A. Galstyan, “Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing,” in International Conference on Machine Learning, 2019, pp. 21–29.
  27. F. Wu, T. Zhang, A. Holanda de Souza, C. Fifty, T. Yu, and K. Q. Weinberger, “Simplifying graph convolutional networks,” Proceedings of Machine Learning Research, 2019.
  28. K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in Proc. of ICLR, 2019.
  29. S. Pan, R. Hu, G. Long, J. Jiang, L. Yao, and C. Zhang, “Adversarially regularized graph autoencoder for graph embedding.” in IJCAI, 2018, pp. 2609–2615.
  30. H. Zhang, R. Zhang, and X. Li, “Embedding graph auto-encoder for graph clustering,” arXiv preprint arXiv:2002.08643, 2020.
  31. D. Bo, X. Wang, C. Shi, M. Zhu, E. Lu, and P. Cui, “Structural deep clustering network,” in WWW.   ACM / IW3C2, 2020, pp. 1400–1410.
  32. J. Park, M. Lee, H. J. Chang, K. Lee, and J. Y. Choi, “Symmetric graph convolutional autoencoder for unsupervised graph representation learning,” in Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 6519–6528.
  33. D. Dua and C. Graff, “UCI machine learning repository,” 2017. [Online]. Available: http://archive.ics.uci.edu/ml
  34. C. Hou, F. Nie, X. Li, D. Yi, and Y. Wu, “Joint embedding learning and sparse regression: A framework for unsupervised feature selection,” IEEE Transactions on Cybernetics, vol. 44, no. 6, pp. 793–804, 2014.
  35. M. Lyons, J. Budynek, and S. Akamatsu, “Automatic classification of single facial images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1357–1362, 1999.
  36. S. Nene, S. Nayar, and H. Murase, “Columbia object image library (coil-20),” Technical Report CUCS-005-96, 1996.
  37. J. Hull, “A database for handwritten text recognition research,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 5, pp. 550–554, 1994.
  38. Y. LeCun, “The mnist database of handwritten digits,” 1998. [Online]. Available: http://yann.lecun.com/exdb/mnist/
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Authors (3)
  1. Xuelong Li (268 papers)
  2. Hongyuan Zhang (31 papers)
  3. Rui Zhang (1138 papers)
Citations (64)
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