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Community detection in attributed networks via adaptive deep nonnegative matrix factorization

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Abstract

Community detection plays an important role in analyzing attributed networks. It attempts to find the optimal cluster structures to identify valuable information. Although deep nonnegative matrix factorization (DNMF) is widely used in community detection, it cannot be used to analyze attributed networks since only topology information is considered. Recent researches have taken attribute information into account, but we still need to face the following challenges. First, it is difficult to deal with topology noise and attribute noise in attributed networks at one stroke. Second, we need to balance the coupling between topology and node attributes with hyperparameters in most methods. However, with inappropriate hyperparameters, it is easy to cause interference and compromise between them. For the above challenges, in this paper, we propose a novel method, namely adaptive deep nonnegative matrix factorization. Specifically, we handle the inherent noise of attributed networks via dual-DNMF with autoencoder. And then, we use the attention mechanism to adaptively integrate topology information and attribute information without adjusting hyperparameters manually. Overall, our method not only handles the inherent noise in attributed networks, but also resolves the interference and compromise between topology and attributes in a generalized way. The results of comprehensive experiments support our conclusions and demonstrate that our method outperforms the state-of-the-art methods in most datasets.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Teng XY, Liu J, Li MM (2021) Overlapping community detection in directed and undirected attributed networks using a multiobjective evolutionary algorithm. IEEE Trans Cybern 51(1):138–150

    Article  Google Scholar 

  2. Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barab AL (2015) Uncovering disease-disease relationships through the incomplete interactome. Am Assoc Adv Sci 347(6224):1257601

    Google Scholar 

  3. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations (ICLR)

  4. Liu FZ, Li Z, Wang BK, Wu J, Yang J, Huang JM, Zhang YQ, Wang WQ, Xue S, Nepal S, Sheng QZ (2022) eRiskCom: an e-commerce risky community detection platform. VLDB J 31:1085–1101

    Article  Google Scholar 

  5. Xu SY, Yang C, Shi C, Fang Y, Guo YX, Yang TC, Zhang LH, Hu MD (2021) Topic-aware heterogeneous graph neural network for link prediction. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 2261–2270

  6. Cen YK, Zou X, Zhang JW, Yang HX, Zhou JR, Tang J (2019) Representation learning for attributed multiplex heterogeneous network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1358–1368

  7. Su X, Xue S, Liu FZ, Wu J, Yang J, Zhou C, Hu WB, Paris C, Nepal S, Jin D, Sheng QZ, Yu PS (2021) A comprehensive survey on community detection with deep learning. IEEE Trans Neural Netw Learn Syst abs/2105.12584

  8. Jin D, Yu ZZ, Jiao PF, Pan SR, Yu PS, Zhang WX (2021) A survey of community detection approaches: From statistical modeling to deep learning. IEEE Trans Knowl Data Eng abs/2101.01669

  9. Liu FZ, Xue S, Wu J, Zhou C, Hu WB, Paris C, Nepal S, Yang J, Yu PS (2020) Deep learning for community detection: progress, challenges and opportunities. In: Proceedings of the twenty-ninth international joint conference on artificial intelligence, pp 4981–4987

  10. Cheng JW, Li WS, Han KL, Tang Y, He CB, Zhang NN (2022) SARNMF: a community detection method for attributed networks. In: 2022 IEEE 25th international conference on computer supported cooperative work in design(IEEE CSCWD 2022), pp 879–884

  11. Ma XK, Dong D, Wang Q (2019) Community detection in multi-layer networks using joint nonnegative matrix factorization. IEEE Trans Knowl Data Eng 31(2):273–286

    Article  Google Scholar 

  12. Jin D, He J, Chai BF, He DX (2021) Semi-supervised community detection on attributed networks using non-negative matrix tri-factorization with node popularity. Front Comp Sci 15(4):1–11

    Google Scholar 

  13. He DX, Song Y, Feng ZY, Zhang BB, Yu ZZ, Zhang WX (2020) Community-centric graph convolutional network for unsupervised community detection. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp 3515–3521

  14. Oleksandr S, Günnemann S (2019) Overlapping community detection with graph neural networks. preprint arXiv

  15. Yang L, Zhou WM, Peng WH (2022) Graph neural networks beyond compromise between attribute and topology. In: Proceedings of the ACM web conference, pp 1127–1135

  16. Liu FZ, Wu J, Xue S, Zhou C, Yang J, Sheng QZ (2020) Detecting the evolving community structure in dynamic social networks. World Wide Web 23(2):715–733

    Article  Google Scholar 

  17. Liu FZ, Wu J, Zhou C, Yang J (2019) Evolutionary community detection in dynamic social networks. 2019 international joint conference on neural networks, pp 1–7

  18. He CB, Fei X, Cheng QW, Li HC, Hu Z, Tang Y (2022) A survey of community detection in complex networks using nonnegative matrix factorization. IEEE Trans Comput Soc Syst 9(2):440–457

    Article  Google Scholar 

  19. Sun BJ, Shen HW, Gao JH, O WT, Cheng XQ (2017) A non-negative symmetric encoder–decoder approach for community detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 597–606

  20. Wang X, Jin D, Cao XC, Yang L, Z WX (2016) Semantic community identification in large attribute networks. In: Proceedings of the AAAI conference on artificial intelligence 30(1)

  21. He CB, Zheng YL, Fei X, Li HC, Hu Z, Tang Y (2021) Boosting nonnegative matrix factorization based community detection with graph attention auto-encoder. IEEE Trans Big Data 8:968–981

    Article  Google Scholar 

  22. Ji D, Liu Z, He RF, Wang X, He DX (2018) A robust and strong explanation community detection mehtod for attributed networks. Chin J Comput 41(7):1476–1489

    Google Scholar 

  23. Yang L, Chen ZY, Gu JH, Guo YF (2019) Dual self-paced graph convolutional network: towards reducing attribute distortions induced by topology. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, pp 4062–4069

  24. Wang WJ, Liu X, Jiao PF, Chen X, Jin D (2018) A unified weakly supervised framework for community detection and semantic matching. Adv Knowl Discov Data Min 10939:218–230

    Article  Google Scholar 

  25. Li HJ, Huang L, Wang CD, Huang D, Lai HJ, Chen P (2021) Attributed network embedding with micro-meso structure. ACM Trans Knowl Discov Data 15(4):1–26

    Article  Google Scholar 

  26. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27:415–444

    Article  Google Scholar 

  27. Garza SE, Schaeffer SE (2019) Community detection with the label propagation algorithm: a survey. Phys A 534:122058

    Article  MathSciNet  Google Scholar 

  28. 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

  29. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791

    Article  Google Scholar 

  30. Yuan ZJ, Oja E (2005) Projective nonnegative matrix factorization for image compression and feature extraction. Image Analysis, 14th Scandinavian Conference 3540:333–342

  31. Wang X, Cui P, Wang J, Pei J, Yang SQ (2017) Community preserving network embedding. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 203–209

  32. Huang ZH, Zhong XX, Wang Q, Gong MG, Ma XK (2020) Detecting community in attributed networks by dynamically exploring node attributes and topological structure. Knowl-Based Syst 196:105760

    Article  Google Scholar 

  33. Jin D, He J, Chai BF, He DX (2021) Semi-supervised community detection on attributed networks using non-negative matrix tri-factorization with node popularity. Front Comp Sci 15(4):154324

    Article  Google Scholar 

  34. Trigeorgis G, Bousmalis K, Zafeiriou S, Schuller BW (2017) A deep matrix factorization method for learning attribute representations. IEEE Trans Pattern Anal Mach Intell 39(3):417–429

    Article  Google Scholar 

  35. Ye FH, Chen C, Zheng ZB (2018) Deep autoencoder-like nonnegative matrix factorization for community detection. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 1393–1402

  36. Huang J, Zhang TH, Yu WH, Zhu J, Cai EC (2020) Community detection based on modularized deep nonnegative matrix factorization. Int J Pattern Recognit Artif Intell 32(5):2159006:1-2159006:17

    Google Scholar 

  37. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  38. Lee DD, Seung HS (2000) Algorithms for non-negative matrix factorization. Adv Neural Inf Proc Syst 13:556–562

    Google Scholar 

  39. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(2):046110

    Article  Google Scholar 

  40. He CB, Zheng YL, Cheng JW, Tang Y, Chen GH, Liu H (2022) Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder. Inf Sci 608:1464–1479

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62077045, and Grant U1811263, in part by the Humanity and Social Science Youth Foundation of Ministry of Education of China under Grant 19YJCZH049.

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Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, 510631, China

    Junwei Cheng, Yong Tang, Chaobo He, Ying Li & Jinhui Wei

  2. Pazhou Lab, Guangzhou, 510335, China

    Junwei Cheng, Yong Tang & Chaobo He

  3. Computer Science Department, University of Southern California, Los Angeles, 90089, USA

    Kunlin Han

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  1. Junwei Cheng
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  2. Yong Tang
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  3. Chaobo He
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Correspondence to Chaobo He.

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Cheng, J., Tang, Y., He, C. et al. Community detection in attributed networks via adaptive deep nonnegative matrix factorization. Neural Comput & Applic 36, 897–912 (2024). https://doi.org/10.1007/s00521-023-09066-y

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