Skip to main content
SpringerLink
Log in

MedGraph: malicious edge detection in temporal reciprocal graph via multi-head attention-based GNN

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the popularity of various online dating applications, it has become a crucial task to detect anomalous or malicious users from a large number of reciprocal users. Essentially, this task could be converted to a malicious edge detection problem, which is an important yet challenging task due to the following difficulties. First, malicious users may fake their profiles to avoid being detected by the service platform. Second, malicious behaviors, i.e., malicious edges, might vary from time to time which greatly challenges most existing approaches. To address the aforementioned issues, this paper proposes the multi-head attention-based GNN approach to detect malicious edges from a temporal reciprocal graph, called MedGraph. Particularly, the proposed MedGraph approach employs a transformer component to first capture both long-term and short-term behavior characteristics of malicious users from their historical interaction data. Then, a co-attention component is designed to differentiate important features that account for the prediction of malicious edges. We evaluate the proposed approach on two public datasets and one real-world dataset collected by ourselves. The promising results demonstrate that our approach could achieve state-of-the-art performance against a number of both baseline and SOTA approaches with respect to the widely adopted evaluation criteria.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The authors declare that the “UCI message dataset” and the “Digg dataset” supporting the findings of this study are available within the article. The private reciprocal dataset that support the findings of this study is available on request from the corresponding author. The data are not publicly available due to the containing information that could compromise research participant privacy.

Notes

  1. http://www.digg.com.

  2. http://www.zhenai.com/.

References

  1. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(3):1–58

    Article  Google Scholar 

  2. Sánchez PI, Müller E, Irmler O, Böhm K (2014) Local context selection for outlier ranking in graphs with multiple numeric node attributes. In: Proceedings of the 26th international conference on scientific and statistical database management, pp 1–12

  3. Bhuyan MH, Bhattacharyya DK, Kalita JK (2013) Network anomaly detection: methods, systems and tools. IEEE Commun Surv Tutor 16(1):303–336

    Article  Google Scholar 

  4. Noble CC, Cook DJ (2003) Graph-based anomaly detection. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, pp 631–636

  5. Ahmed M, Mahmood AN, Hu J (2016) A survey of network anomaly detection techniques. J Netw Comput Appl 60:19–31

    Article  Google Scholar 

  6. Zheng L, Li Z, Li J, Li Z, Gao J (2019) Addgraph: anomaly detection in dynamic graph using attention-based temporal gcn. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. AAAI Press, pp 4419–4425

  7. Akoglu L, Tong H, Koutra D (2015) Graph based anomaly detection and description: a survey. Data Min Knowl Discov 29(3):626–688

    Article  MathSciNet  Google Scholar 

  8. Ranshous S, Shen S, Koutra D, Harenberg S, Faloutsos C, Samatova NF (2015) Anomaly detection in dynamic networks: a survey. Wiley Interdiscip Rev Comput Stat 7(3):223–247

    Article  MathSciNet  Google Scholar 

  9. Hooi B, Song HA, Beutel A, Shah N, Shin K, Faloutsos C (2016) Fraudar: bounding graph fraud in the face of camouflage. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 895–904

  10. McConville R, Liu W, Miller P (2015) Vertex clustering of augmented graph streams. In: Proceedings of the 2015 SIAM international conference on data mining. SIAM, pp 109–117

  11. Zhao Y, Yu PS (2013) On graph stream clustering with side information. In: Proceedings of the 2013 SIAM international conference on data mining. SIAM, pp 139–150

  12. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907

  13. Yu W, Cheng W, Aggarwal CC, Zhang K, Chen H, Wang W (2018) Netwalk: a flexible deep embedding approach for anomaly detection in dynamic networks. In: SIGKDD, pp 2672–2681

  14. Li D, Chen D, Jin B, Shi L, Goh J, Ng S-K (2019) Mad-gan: multivariate anomaly detection for time series data with generative adversarial networks. In: International conference on artificial neural networks. Springer, pp 703–716

  15. Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80

    Article  Google Scholar 

  16. Benson AR, Gleich DF, Leskovec J (2016) Higher-order organization of complex networks. Science 353(6295):163–166

    Article  Google Scholar 

  17. Luo L, Liu K, Peng D, Ying Y, Zhang X (2020) A motif-based graph neural network to reciprocal recommendation for online dating. In: Proceedings of the international conference on neural information processing, pp 102–114

  18. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: SIGKDD, pp 701–710

  19. 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

  20. Guthrie D, Allison B, Liu W, Guthrie L, Wilks Y (2006) A closer look at skip-gram modelling. In: LREC, pp 1222–1225

  21. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

  22. Opsahl T, Panzarasa P (2009) Clustering in weighted networks. Soc Netw 31(2):155–163

    Article  Google Scholar 

  23. Aggarwal CC, Zhao Y, Philip SY (2011) Outlier detection in graph streams. In: 2011 IEEE 27th international conference on data engineering. IEEE, pp 399–409

  24. Ranshous S, Harenberg S, Sharma K, Samatova NF (2016) A scalable approach for outlier detection in edge streams using sketch-based approximations. In: Proceedings of the 2016 SIAM international conference on data mining. SIAM, pp 189–197

  25. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1024–1034

  26. Hou D, Cong Y, Sun G, Dong J, Li J, Li K (2020) Fast multi-view outlier detection via deep encoder. IEEE Trans Big Data 1:1

    Google Scholar 

  27. Huang C, Min G, Wu Y, Ying Y, Pei K, Xiang Z (2017) Time series anomaly detection for trustworthy services in cloud computing systems. IEEE Trans Big Data 8:60–72

    Article  Google Scholar 

  28. Ling Z, Qiu RC, He X, Chu L (2019) A new approach of exploiting self-adjoint matrix polynomials of large random matrices for anomaly detection and fault location. IEEE Trans Big Data 7:548–558

    Article  Google Scholar 

  29. Chen X, Song X, Ren R, Zhu L, Cheng Z, Nie L (2020) Fine-grained privacy detection with graph-regularized hierarchical attentive representation learning. ACM Trans Inf Syst 38:1–26

    Google Scholar 

  30. Abbasi A, Zahedi FM, Kaza S (2012) Detecting fake medical web sites using recursive trust labeling. ACM Trans Inf Syst 30(4):1–16. https://doi.org/10.1145/2382438.2382441

    Article  Google Scholar 

  31. Liu Y, Wu Y-FB (2020) Fned: a deep network for fake news early detection on social media. ACM Trans Inf Syst (TOIS) 38(3):1–33

    Article  MathSciNet  Google Scholar 

  32. Bilgin CC, Yener B (2006) Dynamic network evolution: models, clustering, anomaly detection. IEEE Netw (1)

  33. Wang H, Tang M, Park Y, Priebe CE (2013) Locality statistics for anomaly detection in time series of graphs. IEEE Trans Signal Process 62(3):703–717

    Article  MathSciNet  MATH  Google Scholar 

  34. Shin K, Hooi B, Faloutsos C (2016) M-zoom: fast dense-block detection in tensors with quality guarantees. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, pp 264–280

  35. Cormode G, Muthukrishnan S (2005) An improved data stream summary: the count-min sketch and its applications. J Algorithms 55(1):58–75

    Article  MathSciNet  MATH  Google Scholar 

  36. Chang S, Han W, Tang J, Qi G-J, Aggarwal CC, Huang TS (2015) Heterogeneous network embedding via deep architectures. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 119–128

  37. Ergen T, Mirza AH, Kozat SS (2017) Unsupervised and semi-supervised anomaly detection with lstm neural networks. arXiv preprint arXiv:1710.09207

  38. Economides MJ, Nolte KG et al (1989) Reservoir stimulation, vol 2. Prentice Hall, Englewood Cliffs

    Google Scholar 

  39. Ng A et al (2011) Sparse autoencoder. CS294A Lect Notes 72:1–19

    Google Scholar 

  40. Ailon N, Jaiswal R, Monteleoni C (2009) Streaming k-means approximation. In: Advances in neural information processing systems, pp 10–18

  41. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China

    Kai Chen, Ziao Wang, Xiaofeng Zhang & Linhao Luo

  2. Artificial Intelligence Department, PingAn Life, Shenzhen, Guangdong, China

    Kai Liu

Authors
  1. Kai Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaofeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Linhao Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaofeng Zhang.

Ethics declarations

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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

Chen, K., Wang, Z., Liu, K. et al. MedGraph: malicious edge detection in temporal reciprocal graph via multi-head attention-based GNN. Neural Comput & Applic 35, 8919–8935 (2023). https://doi.org/10.1007/s00521-022-08065-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08065-9

Keywords

Search

Navigation