Skip to main content
Springer Nature Link
Log in

Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection

  • Conference paper
  • First Online:
Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2012))

  • 564 Accesses

Abstract

Community detection is widely used in network analysis, which seeks to divide network nodes into distinct communities based on the topology structure and attribute information of the network. Due to its interpretability, nonnegative matrix factorization becomes an essential method for community detection. However, it decomposes the adjacency matrix and attribute matrix separately, which do not tightly incorporate topology and attributes. And in the problem of division inconsistency based on topology and attributes caused by the mismatch between the topology similarity and attribute similarity of paired nodes, it ignores the difference in the matching degree of each attribute and each node. In this paper, we propose a nonnegative matrix factorization algorithm for community detection (MTACD) based on the matching degree between topology and attribute. First, we employ an attribute embedding mechanism to enhance the node-attribute relationship. Second, we design an attribute matching degree and a node topology-and-attribute matching degree in order to resolve the mismatch between topology and attribute similarity. Experiments on both real-world and synthetic networks demonstrate the effectiveness of our algorithm.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang, Y., Zhang, Y.: Nonnegative matrix factorization: a comprehensive review. IEEE Trans. Knowl. Data Eng. 25(6), 1336–1353 (2012)

    Article  Google Scholar 

  2. Seung, D., Lee, L.: Algorithms for non-negative matrix factorization. Adv. Neural. Inf. Process. Syst. 13, 556–562 (2001)

    Google Scholar 

  3. Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.: Network representation learning with rich text information. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 2111–2117 (2015)

    Google Scholar 

  4. Huang, X., Li, J., Hu, X.: Accelerated attributed network embedding. In: Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 633–641 (2017)

    Google Scholar 

  5. Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., Tang, J.: Network embedding as matrix factorization: unifying DeepWalk, LINE, PTE, and Node2vec. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining, pp. 459–467 (2018)

    Google Scholar 

  6. 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, pp. 701–710 (2014)

    Google Scholar 

  7. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)

    Google Scholar 

  8. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)

    Google Scholar 

  9. Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)

    Google Scholar 

  10. Ye, F., Chen, C., Zheng, Z.: 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 (2018)

    Google Scholar 

  11. Wang, X., Jin, D., Cao, X., Yang L., Zhang, W.: Semantic community identification in large attribute networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)

    Google Scholar 

  12. Li, Y., Sha, C., Huang, X., Zhang, Y.: Community detection in attributed graphs: an embedding approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  13. Qin, M., Jin, D., He D, Gabrys, B., Musial, K.: Adaptive community detection incorporating topology and content in social networks. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 675–682 (2017)

    Google Scholar 

  14. Zhao, Z., Ke, Z., Gou, Z., Guo, H., Jiang, K., Zhang, R.: The trade-off between topology and content in community detection: an adaptive encoder-decoder-based NMF approach. Expert Syst. Appl. 209, 118230 (2022)

    Article  Google Scholar 

  15. Meng, Q., Kai, L.: Dual-channel hybrid community detection in attributed networks. Inf. Sci. 551, 146–167 (2021)

    Article  MathSciNet  Google Scholar 

  16. Newman, M.E.J.: Mixing patterns in networks. Phys. Rev. E 67(2), 026126 (2003)

    Article  MathSciNet  Google Scholar 

  17. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)

    Article  Google Scholar 

  18. Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)

    Article  Google Scholar 

  19. Huang, B., Wang, C., Wang, B.: NMLPA: uncovering overlapping communities in attributed networks via a multi-label propagation approach. Sensors 19(2), 260 (2019)

    Article  Google Scholar 

  20. Chakraborty, T., Dalmia, A., Mukherjee, A., Ganguly, N.: Metrics for community analysis: a survey. ACM Comput. Surv. (CSUR) 50(4), 1–37 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 62002063 and No. U21A20472, the National Key Research and Development Plan of China under Grant No. 2021YFB3600503, 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. 2022J01118, No. 2020J05112 and No. 2020J01420, the Fujian Provincial Department of Education under Grant No. JAT190026, the Major Science and Technology Project of Fujian Province under Grant No. 2021HZ022007 and Haixi Government Big Data Application Cooperative Innovation Center.

Author information

Authors and Affiliations

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

    Ruolan Zeng, Zhanghui Liu & Kun Guo

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

    Ruolan Zeng, Zhanghui Liu & Kun Guo

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

    Ruolan Zeng, Zhanghui Liu & Kun Guo

Authors
  1. Ruolan Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhanghui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kun Guo .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Harbin Engineering University, Harbin, China

    Tong Wang

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  6. Tongji University, Shanghai, China

    Bowen Du

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zeng, R., Liu, Z., Guo, K. (2024). Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-9637-7_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9636-0

  • Online ISBN: 978-981-99-9637-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships