Skip to main content
SpringerLink
Log in

An autoencoder considering multi-order and structural-role similarity for community detection in attributed networks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

A community is composed of closely related nodes. Detecting communities in a network has many practical applications, such as online product recommendation, biological molecule discovery and criminal group tracking. In recent years, network representation learning (NRL) has attracted much attention in the field of community detection because it can effectively extract complex relations between nodes which improves the quality of detected communities. In many real-world networks, the rich attribute information contained in nodes and the similarity between nodes and their multi-order neighbors has significant contributions to the generation of node embedding vectors in NRL. However, existing NRL algorithms treat a node’s different order neighbors equally as high-order contexts, leading to the ignorance of their different impacts on the generation of the node’s embedding vector. In addition, these algorithms do not focus on the coupling and interaction relations between nodes playing similar structural roles, which may ignore some nodes in a community with similar structural roles. In this paper, we propose a novel autoencoder considering the multi-order similarity and structural role similarity (AMOSOS) to solve the above problems. First, we design a strategy to obtain a multi-order weight matrix which preserves the differential influence of neighbors of different orders by sequentially decreasing the weight of each order. Second, we design a role similarity indicator to capture the complex coupling and interaction relations of nodes in the network. Experimental results on synthetic networks and real-world networks show that our proposed algorithm is more accurate than existing network representation learning algorithms for the task of community detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

Data are available on request to the authors.

References

  1. Bandyopadhyay S, Biswas A, Kara H, Murty M (2020) A multilayered informative random walk for attributed social network embedding. In: ECAI 2020, pp 1738–1745. IOS Press

  2. Bezdek JC, Ehrlich R, Full W (1984) Fcm: the fuzzy c-means clustering algorithm. Comput Geosci 10(2-3):191–203

    Article  Google Scholar 

  3. Cai H, Zheng VW, Chang KCC (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616–1637

    Article  Google Scholar 

  4. Cavallari S, Zheng VW, Cai H, Chang KCC, Cambria E (2017) Learning community embedding with community detection and node embedding on graphs. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 377–386

  5. Cui P, Wang X, Pei J, Zhu W (2018) A survey on network embedding. IEEE Trans Knowl Data Eng 31(5):833–852

    Article  Google Scholar 

  6. Duan D, Tong L, Li Y, Lu J, Shi L, Zhang C (2020) Aane: anomaly aware network embedding for anomalous link detection. In: 2020 IEEE international conference on data mining (ICDM). IEEE, pp 1002–1007

  7. Fan H, Zhang F, Li Z (2020) Anomalydae: dual autoencoder for anomaly detection on attributed networks. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 5685–5689

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

  9. Gao M, Chen L, He X, Zhou A (2018) Bine: Bipartite network embedding. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 715–724

  10. Gao Y, Gong M, Xie Y, Zhong H (2020) Community-oriented attributed network embedding. Knowl-Based Syst 193(105):418

    Google Scholar 

  11. Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 855–864

  12. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst:30

  13. He D, Feng Z, Jin D, Wang X, Zhang W (2017) Joint identification of network communities and semantics via integrative modeling of network topologies and node contents. In: Thirty-first AAAI conference on artificial intelligence

  14. Huang X, Li J, Hu X (2017) Label informed attributed network embedding. In: Proceedings of the tenth ACM international conference on web search and data mining, pp 731–739

  15. Jin D, Li B, Jiao P, He D, Zhang W (2019) Network-specific variational auto-encoder for embedding in attribute networks. In: IJCAI, pp 2663–2669

  16. Ketkar N (2017) Stochastic gradient descent. In: Deep learning with python. Springer, pp 113–132

  17. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Physical review E 78(4):046,110

    Article  Google Scholar 

  18. Li PZ, Huang L, Wang CD, Huang D, Lai JH (2018) Community detection using attribute homogenous motif. IEEE Access 6:47,707–47,716

    Article  Google Scholar 

  19. Li W, Qin M, Lei K (2019) Identifying interpretable link communities with user interactions and messages in social networks. In: 2019 IEEE Intl conf on parallel & distributed processing with applications, big data & cloud computing, sustainable computing & communications, social computing & networking (ISPA/BDCloud/socialcom/sustaincom). IEEE, pp 271–278

  20. Li Z, Wang X, Li J, Zhang Q (2021) Deep attributed network representation learning of complex coupling and interaction. Knowl-Based Syst 212(106):618

    Google Scholar 

  21. McCallum AK, Nigam K, Rennie J, Seymore K (2000) Automating the construction of internet portals with machine learning. Inf Retr 3(2):127–163

    Article  Google Scholar 

  22. Nan DY, Yu W, Liu X, Zhang YP, Dai WD (2018) A framework of community detection based on individual labels in attribute networks. Physica A: Stat Mech Appl 512:523–536

    Article  Google Scholar 

  23. Ozer M, Kim N, Davulcu H (2016) Community detection in political twitter networks using nonnegative matrix factorization methods. In: 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 81–88

  24. Pan S, Hu R, Fung SF, Long G, Jiang J, Zhang C (2019) Learning graph embedding with adversarial training methods. IEEE Trans Cybern 50(6):2475–2487

    Article  Google Scholar 

  25. Pan Y, He F, Yu H (2020) Learning social representations with deep autoencoder for recommender system. World Wide Web 23(4):2259–2279

    Article  Google Scholar 

  26. Pei Y, Du X, Zhang J, Fletcher G, Pechenizkiy M (2020) struc2gauss: structural role preserving network embedding via gaussian embedding. Data Min Knowl Disc 34(4):1072–1103

    Article  MathSciNet  Google Scholar 

  27. 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

  28. Qiu J, Dong Y, Ma H, Li J, Wang K, Tang J (2018) Network embedding as matrix factorization: unifying deepwalk, line, pte, and node2vec. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 459–467

  29. Shi X, Lu H, He Y, He S (2015) Community detection in social network with pairwisely constrained symmetric non-negative matrix factorization. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp 541–546

  30. Soriano-Sánchez A, Posadas-Castillo C (2018) Smart pattern to generate small–world networks. Chaos, Solitons Fractals 114:415–422

    Article  MathSciNet  MATH  Google Scholar 

  31. Sun FY, Qu M, Hoffmann J, Huang CW, Tang J (2019) vgraph: a generative model for joint community detection and node representation learning. Adv Neural Inf Process Syst:32

  32. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, pp 1067–1077

  33. Tu C, Zeng X, Wang H, Zhang Z, Liu Z, Sun M, Zhang B, Lin L (2018) A unified framework for community detection and network representation learning. IEEE Trans Knowl Data Eng 31(6):1051–1065

    Article  Google Scholar 

  34. Wang X, Jin D, Cao X, Yang L, Zhang W (2016) Semantic community identification in large attribute networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 30

  35. Wang X, Jin D, Cao X, Yang L, Zhang W (2016) Semantic community identification in large attribute networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 30

  36. Yang C, Liu Z, Zhao D, Sun M, Chang E (2015) Network representation learning with rich text information. In: Twenty-fourth international joint conference on artificial intelligence

  37. Yang Z, Cohen W, Salakhudinov R (2016) Revisiting semi-supervised learning with graph embeddings. In: International conference on machine learning. PMLR, pp 40–48

  38. Zhang C, Liu Y, Fu H (2019) Ae2-nets: Autoencoder in autoencoder networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2577–2585

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

  40. Zhang Z, Yang H, Bu J, Zhou S, Yu P, Zhang J, Ester M, Wang C (2018) Anrl: Attributed network representation learning via deep neural networks. In: Ijcai, vol 18, pp 3155–3161

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No.61672159, No.61672158, No.61300104 and No.62002063, the Fujian Collaborative Innovation Center for Big Data Applications in Governments, the Fujian Industry-Academy Cooperation Project under Grant No.2017H6008 and No.2018H6010, the Natural Science Foundation of Fujian Province under Grant No.2018J07005, No.2019J01835,No.2020J05112 and No.2020J01230054, the Fujian Provincial Department of Education under Grant No.JAT190026, the Fuzhou University under Grant 510872/GXRC-20016 and Haixi Government Big Data Application Cooperative Innovation Center and the China Scholarship Council under Grant 202006655008.

Author information

Authors and Affiliations

  1. College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China

    Kun Guo, Gaosheng Lin & Ling Wu

  2. Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou, 350108, China

    Kun Guo, Gaosheng Lin & Ling Wu

  3. Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350108, China

    Kun Guo & Ling Wu

Authors
  1. Kun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaosheng Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ling Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ling Wu.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, K., Lin, G. & Wu, L. An autoencoder considering multi-order and structural-role similarity for community detection in attributed networks. Appl Intell 53, 20365–20381 (2023). https://doi.org/10.1007/s10489-023-04450-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-04450-6

Keywords

Search

Navigation