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Hierarchical attention network for attributed community detection of joint representation

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Abstract

Attributed community detection is a challenging task as it requires joint modelling of graph structure and node attributes. Recent progress on graph neural network (GNN) has proved that it is effective in combining structural and content information, and several GNN-based methods have achieved promising clustering performance on real-world attributed network. Most of the existing community detection methods adopt non-clustering-oriented representation learning methods, which is incapable of capturing interaction between embedding and clustering, thus resulting in suboptimal outcome. In this paper, we propose a novel Hierarchical Attention Network (HiAN) solution for attributed community detection, and unique hierarchical attention network may significantly change the community detection paradigm. In order to fuse rich interpretable interactive information, the hierarchical attentive aggregator in HiAN is designed to learn node representation at both attribute-and structure-spaces. More importantly, the self-training process is jointly learned and optimized with embedding representation in a unified community detection framework, to mutually benefit both components. The experimental results demonstrate that HiAN outperforms several state-of-the-art baselines on four real-world datasets.

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Notes

  1. https://linqs.soe.ucsc.edu/data.

  2. http://snap.stanford.edu/data.

References

  1. Bo D, Wang X, Shi C et al (2020) Structural deep clustering network. Proceedings of the web conference, vol 2020, pp 1400–1410

  2. Cai X, Huang D, Wang CD et al (2020) Spectral clustering by subspace randomization and graph fusion for high-dimensional data. Adv Knowl Disc Data Min 12084:330

  3. Cavallari S, Zheng VW, Cai H et al (2017) Learning community embedding with community detection and node embedding on graphs. Proceedings of the 2017 ACM on conference on information and knowledge management, pp 377–386

  4. Chen H, Yu Z, Yang Q et al (2020) Attributed graph clustering with subspace stochastic block model. Inf Sci 535:130–141

    Article  MathSciNet  Google Scholar 

  5. Comito C (2020) NexT: A framework for next-place prediction on location based social networks. Knowl Based Syst 204:106205

    Article  Google Scholar 

  6. Fan S, Wang X, Shi C et al (2020) One2multi graph autoencoder for multi-view graph clustering. Proceedings of the web conference 2020. pp 3070–3076

  7. Gao H, Huang H (2018) Deep attributed network embedding. Twenty-seventh international joint conference on artificial intelligence (IJCAI)). 2018

  8. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 2010, pp 249–256

  9. Hatcher WG, Yu W (2018) A survey of deep learning: Platforms, applications and emerging research trends. IEEE Access 6:24411–24432

    Article  Google Scholar 

  10. Huang X, Li J, Hu X. Accelerated attributed network embedding. In: Proceedings of the 2017 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, 2017: 633–641

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

    Article  Google Scholar 

  12. Kim B, Khanna R, Koyejo OO (2016) Examples are not enough, learn to criticize! criticism for interpretability. Adv Neural Inf Process Syst 29

  13. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: 3rd international conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015

  14. Liao L, He X, Zhang H et al (2018) Attributed social network embedding. IEEE Trans Knowl Data Eng 30(12):2257–2270

    Article  Google Scholar 

  15. Liu F, Xue S, Wu J, Zhou C, Hu W, Paris C, Nepal S, Yang J, Philip S. Deep learning for community detection: progress, challenges and opportunities. In: Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI 2020, pp 4981–4987

  16. Liu L, Xu L, Wangy Z et al (2015) Community detection based on structure and content: a content propagation perspective. 2015 IEEE international conference on data mining. IEEE, pp 271–280

  17. Luo D, Ni J, Wang S et al (2020) Deep multi-graph clustering via attentive cross-graph association. Proceedings of the 13th international conference on web search and data mining, pp 393–401

  18. Mehta N, Duke LC, Rai P (2019) Stochastic blockmodels meet graph neural networks. International conference on machine learning. PMLR, pp 4466–4474

  19. Newman Mark EJ (2010) Networks: an introduction. Oxford University Press, Oxford

    Book  Google Scholar 

  20. Newman MEJ, Clauset A (2016) Structure and inference in annotated networks. Nat Commun 7(1):1–11

  21. Peng S, Yang A, Cao L et al (2017) Social influence modeling using information theory in mobile social networks. Inf Sci 379:146–159

    Article  Google Scholar 

  22. Perozzi B, Akoglu L (2016) Scalable anomaly ranking of attributed neighborhoods. In: Proceedings of the 2016 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, pp 207–215

  23. Qiao M, Yu J, Bian W et al (2018) Adapting stochastic block models to power-law degree distributions. IEEE Trans Cybern 49(2):626–637

    Article  Google Scholar 

  24. Shalizi CR, Thomas AC (2011) Homophily and contagion are generically confounded in observational social network studies. Sociol Methods Res 40(2):211–239

  25. Stanley N, Bonacci T, Kwitt R et al (2019) Stochastic block models with multiple continuous attributes. Appl Netw Sci 4(1):1–22

    Article  Google Scholar 

  26. Tian F, Gao B, Cui Q et al (2014) Learning deep representations for graph clustering. In: Proceedings of the AAAI conference on artificial intelligence, 28(1)

  27. Wang C, Pan S, Hu R, Long G, Jiang J, Zhang C (2019) Attributed graph clustering: a deep attentional embedding approach. In: proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI 2019, Macao, China, August 10–16, 2019, pp 3670–3676

  28. Wang X, Ji H, Shi C et al (2019) Heterogeneous graph attention network. The World Wide Web conference, pp 2022–2032

  29. Whang JJ, Hou Y, Gleich DF et al (2018) Non-exhaustive, overlapping clustering. IEEE Trans Pattern Anal Mach Intell 41(11):2644–2659

    Article  Google Scholar 

  30. Wu Z, Pan S, Chen F et al (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24

  31. Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. International conference on machine learning. PMLR, pp 478–487

  32. Yang J, McAuley J, Leskovec J (2013) Community detection in networks with node attributes. 2013 IEEE 13th international conference on data mining. IEEE, pp 1151–1156

  33. You X, Ma Y, Liu Z (2020) A three-stage algorithm on community detection in social networks. Knowl Based Syst 187:104822

  34. Zhang H, Wang S, Xu X et al (2018) Tree2Vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 29(11):5304–5318

    Article  MathSciNet  Google Scholar 

  35. Zhang XL (2018) Multilayer bootstrap networks. Neural Netw 103:29–43

    Article  Google Scholar 

  36. Zhang Y, Lyu T, Zhang Y (2018) Cosine: community-preserving social network embedding from information diffusion cascades. Proceedings of the AAAI conference on artificial intelligence, 32(1)

  37. Zhang Z, Cui P, Zhu W (2020) Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng

  38. Zhe C, Sun A, Xiao X (2019) Community detection on large complex attribute network. Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2041–2049

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61762078, 61363058, 61966004), Natural Science Foundation of Gansu Province(21JR7RA114), Northwest Normal University young teachers research capacity promotion plan (NWN-ULKQN2019-2) and Research Fund of Guangxi Key Laboratory of Trusted Software (kx202003).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

    Qiqi Zhao & Huifang Ma

  2. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Huifang Ma

  3. College of Information Science and Engineering, Ningbo University, Ningbo, 315211, China

    Lijun Guo

  4. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Zhixin Li

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  1. Qiqi Zhao
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  2. Huifang Ma
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  4. Zhixin Li
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Correspondence to Huifang Ma.

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Zhao, Q., Ma, H., Guo, L. et al. Hierarchical attention network for attributed community detection of joint representation. Neural Comput & Applic 34, 5587–5601 (2022). https://doi.org/10.1007/s00521-021-06723-y

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