Skip to main content
Springer Nature Link
Log in

Attributed community search considering community focusing and latent relationship

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Attributed community search is to find a subgraph with some specific attributes online in terms of given vertices. It can help us retrieve information on a subgraph rather than the whole graph, thus enable down-stream graph search applications. However, it is difficult for users to specify exact query vertices if they are unfamiliar with the required graph. Most existing community search methods depend on the query vertices strictly and cause the searched community to shift from the truth community. Meanwhile, due to the incompleteness of original graph data, there exist many latent relationships between vertices, which may influence the search results. But most existing methods ignore these latent relationships and usually lead to a result with low F1 scores. Therefore, this research proposes an improved attributed community search method considering community focusing and latent relationships. We first build a structure attribute network embedding model to learn representations for vertices. Based on this model, the latent relationships are discovered and added to the original graph. Then a community shifting correction algorithm is presented to solve community focusing problem and achieve a more desired community. The experimental work on real-world networks confirms that our method can achieve better performance than existing methods.

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

Access this article

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

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Notes

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

References

  1. Yixiang F, Xin H, Qin L, Ying Z, Wenjie Z, Reynold C, Xuemin L (2020) A survey of community search over big graphs. VLDB J 29(1):353–392

    Article  Google Scholar 

  2. Xin H, Hong C, Lu Q, Wentao T, Yu JX (2014) Querying k-truss community in large and dynamic graphs. In: Curtis ED, Feifei L, Tamer M (eds) Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pp 1311–1322

  3. Lizi L, Xiangnan H, Hanwang Z, Tat-Seng C (2018) Attributed social network embedding. IEEE Trans Knowl Data Eng 30(12):2257–2270

    Article  Google Scholar 

  4. Xin H, Lakshmanan LVS (2017) Attribute-driven community search. Proc VLDB Endow 10:949–960

    Article  Google Scholar 

  5. Xin H, Laks VSL, Xu J (2017) Community search over big graphs: models, algorithms, and opportunities. In: 2017 IEEE 33rd international conference on data engineering (ICDE), pp 1451–1454

  6. James C, Yiping K, Shumo C, Ozsu MT (2011) Efficient core decomposition in massive networks. In: Serge A, Klemens B, Christoph K, Kian LT (eds) IEEE 27th international conference on data engineering, pp 51–62

  7. Wang Jia and James Cheng (2012) Truss decomposition in massive networks. Proc VLDB Endow 5(9):812–823

    Article  Google Scholar 

  8. Tsourakakis C, Bonchi F, Gionis A, Gullo F, Tsiarli M (2013) Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees. In: Inderjit SD, Yehuda K, Rayid GT Senator E, Paul B, Rajesh P, Jingrui H, Robert L, Grossman RU (eds) Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 104–112

  9. Lijun C, Xu J, Lu Y, Qin LX, Liu C, Liang W (2013) Efficiently computing k-edge connected components via graph decomposition. In: Kenneth AR, Divesh S, Dimitris P (eds) Proceedings of the 2013 ACM SIGMOD international conference on management of data, pp 205–216

  10. Dong W, Lu Q, Ying Z, Lijun C, Ling C (2019) Enumerating k-vertex connected components in large graphs. In: 2019 IEEE 35th international conference on data engineering (ICDE), pp 52–63

  11. Yixiang F, Cheng R, Luo S, Jiafeng H (2016) Effective community search for large attributed graphs. Proc VLDB Endow 9(12):1233–1244

    Article  Google Scholar 

  12. Qing L, Yifan Z, Minjun Z, Xin H, Jianliang X, Yunjun G (2020) VAC: vertex-centric attributed community search. In: 36th IEEE international conference on data engineering, ICDE 2020, Dal las, TX, USA, pp 937–948

  13. Zhang Z, Huang X, Xu J, Choi B, Shang Z (2019) Keyword-centric community search. In: 2019 IEEE 35th international conference on data engineering (ICDE), pp 422–433

  14. Zhuo W, Wang W, Wang C, Gu X, Li B, Meng D (2019) Community focusing: yet another query-dependent community detection. In: The thirty-third AAAI conference on artificial intelligence, AAAI 2019, Honolulu, Hawaii, USA, January 27-February 1, pp 329–337

  15. Xin H, Laks VSL, Jeffrey XY, Hong C (2015) Approximate closest community search in networks. Proc VLDB Endow 9(4):276–287

    Article  Google Scholar 

  16. Daixin W, Peng C, Wenwu Z (2016) Structural deep network embedding. In: Balaji K, Mohak S, Alexander JS, Haru CA, Dou S, Rajeev R (eds) Proceedings of the 22nd ACM SIGKDD international conference on Know ledge discovery and data mining, pp 1225–1234

  17. Perozzi B, Al-Rfou R, Skiena SD (2014) Online learning of social representations. In: Sofus AM, Claudia P, Jure L, Wei W, Rayid G (eds) Proceedings of the 20th ACM SIGKDD international conference on Know ledge discovery and data mining, pp 701–710

  18. Jian T, Meng Q, Mingzhe W, Ming Z, Jun Y, Qiaozhu M (2015) Line: Large-scale information network embedding. In: Aldo G, Stefano L, Alessandro P (eds) Proceedings of the 24th international conference on world wide web, pp 1067–1077

  19. Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Balaji K, Mohak S, Alexander JS, Charu C, Aggarwal DS, Rajeev R (eds) Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855–864

  20. Jihwan L, Prabhakar S (2018) A3embed: attribute association aware network embedding. In: Pierre-Antoine C, Fabien G, Mounia L, Panagiotis GI (eds) Companion Proceedings of the the Web conference, pp 1243–1251

  21. Hongchang G, Heng H (2018) Deep attributed network embedding. In Jeerme L (eds) IJCAI, pp 3364–3370

  22. Wanyun C, Yanghua X, Haixun W, Yiqi L, Wei W (2013) Online search of overlapping communities. In: Kenneth AR, Divesh S, Dimitris P (eds) Proceedings of the 2013 ACM SIGMOD international conference on Management of data, pp 277–288

  23. Rong-Hua L, Qin L, Yu JX, Rui M (2015) Influential community search in large networks. Proc VLDB Endow 8(5):509–520

    Article  Google Scholar 

  24. Baxter GJ, Dorogovtsev SN, Mendes JFF, Cellai D (2014) Weak percolation on multiplex networks. Phys Rev E 89(4):042801

    Article  Google Scholar 

  25. Jonathan C (2008) Trusses: cohesive subgraphs for social network analysis. National security agency technical report

  26. Shaosheng C, Lu W, Xu Q (2015) Grarep: learning graph representations with global structural information. In: James B, Alistair M, Charu C, Aggarwal de RM, Ravi K, Vanessa M, Timos KS, Jeffrey XY (eds). Proceedings of the 24th ACM international on conference on information and know ledge management, pp 891–900

  27. Jianxin L, Xinjue W, Ke D, Xiaochun Y, Sellis T, Xu JY (2017) Most influential community search over large social networks. In: 2017 IEEE 33rd international conference on data engineering (ICDE), pp 871–882

  28. Page L, Brin S, Rajeev M, Terry W (1999) The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab

Download references

Acknowledgements

We would like to thank Dr. Yixiang Fang for sharing the ACQ codes. This research is supported by National Natural Science Foundation of China (No.61972255), NSFC-Xinjiang Joint Fund Key Program(No.U2003206), Natural Science Foundation of HeiLongJiang Province of China(No. F2017007).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, People’s Republic of China

    Xiaoqin Xie, Jiaming Zhang, Wei Wang & Wu Yang

Authors
  1. Xiaoqin Xie
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jiaming Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wei Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Wu Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xiaoqin Xie.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, X., Zhang, J., Wang, W. et al. Attributed community search considering community focusing and latent relationship. Knowl Inf Syst 64, 799–829 (2022). https://doi.org/10.1007/s10115-022-01654-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-022-01654-z

Keywords