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The deep fusion of topological structure and attribute information for anomaly detection in attributed networks

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Abstract

Attribute network anomaly detection has attracted more and more research attention due to its wide application in social media, financial transactions, and network security. However, most of the existing methods only consider the network structure or attribute information to detect anomalies, ignoring the combined information of the node structure and attributes in the network. A novel anomaly detection method in attributed networks based on walking autoencoder named RW2AEAD is proposed in this paper, considering structure and attribute information. Besides capturing the network’s structural information by random walking, it gets the combined information of structures and the attributes that are closely related to the structures. And then, the structure and combined reconstruction error of node are obtained by inputting into the autoencoder composed of SkipGram and CBOW. In addition, the global attribute reconstruction error of the node is obtained through the multi-layer attribute autoencoder. Finally, the anomaly score of the node comprehensively considers the above three reconstruction errors, and detects anomalous nodes by setting the threshold and the score ranking. Experiments show that the performance of the proposed RW2AEAD is better than other baseline algorithms in four real datasets.

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References

  1. Aggarwal CC (2015) Outlier analysis. In: Data mining. Springer, pp 237–263

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

    Article  MathSciNet  Google Scholar 

  3. Ayadi A, Ghorbel O, Obeid AM, Abid M (2017) Outlier detection approaches for wireless sensor networks: A survey. Comput Netw 129:319–333

    Article  Google Scholar 

  4. Bandyopadhyay S, Lokesh N, Murty MN (2019) Outlier aware network embedding for attributed networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 33. pp 12–19

  5. Breunig MM, Kriegel HP, Ng RT, Sander J (2000) Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data. pp 93–104

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

    Article  Google Scholar 

  7. Comaniciu D, Meer P (1997) Robust analysis of feature spaces: color image segmentation. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 750–755

  8. Ding K, Li J, Liu H (2019) Interactive anomaly detection on attributed networks. In: Proceedings of the twelfth ACM international conference on web search and data mining. pp 357–365

  9. Gao J, Liang F, Fan W, Wang C, Sun Y, Han J (2010) On community outliers and their efficient detection in information networks. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. pp 813–822

  10. Grover A (2016) 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

  11. Jeh G, Widom J (2002) Simrank: a measure of structural-context similarity. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, pp 538–543

  12. Kaja N, Shaout A, Ma D (2019) An intelligent intrusion detection system. Appl Intell 49 (9):3235–3247

    Article  Google Scholar 

  13. Kumagai A, Iwata T, Fujiwara Y (2020)

  14. Lebichot B, Braun F, Caelen O, Saerens M (2016) A graph-based, semi-supervised, credit card fraud detection system. In: International workshop on complex networks and their applications. Springer, pp 721–733

  15. Li J, Dani H, Hu X, Liu H (2017) Radar: Residual analysis for anomaly detection in attributed networks. In: IJCAI, pp 2152–2158

  16. Metsis V, Androutsopoulos I, Paliouras G (2006) Spam filtering with naive bayes-which naive bayes?. In: CEAS, vol 17. Mountain View, pp 28-69

  17. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space arXiv:1301.3781

  18. Miles J (2013) Instagram power, McGraw-Hill Publishing, New York

  19. Morin F, Bengio Y (2005) Hierarchical probabilistic neural network language model. In: Aistats, vol 5. Citeseer, pp 246–252

  20. Nene SA, Nayar SK, Murase H et al (1996) Columbia object image library (coil-100)

  21. Nisha C, Mohan A (2019) A social recommender system using deep architecture and network embedding. Appl Intell 49(5):1937–1953

    Article  Google Scholar 

  22. Peng Z, Luo M, Li J, Liu H, Zheng Q (2018) Anomalous: A joint modeling approach for anomaly detection on attributed networks. In: IJCAI, pp 3513–3519

  23. Perozzi B, Akoglu L (2016) Scalable anomaly ranking of attributed neighborhoods. In: Proceedings of the 2016 SIAM international conference on data mining. SIAM, pp 207–215

  24. 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

  25. Riesen K, Bunke H (2008) Iam graph database repository for graph based pattern recognition and machine learning. In: Joint IAPR international workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition (SSPR). Springer, pp 287–297

  26. Rong X (2014) word2vec parameter learning explained. arXiv:1411.2738

  27. Ruff L, Vandermeulen R, Goernitz N, Deecke L, Siddiqui SA, Binder A, Müller E, Kloft M (2018) Deep one-class classification. In: International conference on machine learning. pp 4393– 4402

  28. Sánchez PI, Müller E, Laforet F, Keller F, Böhm K (2013) Statistical selection of congruent subspaces for mining attributed graphs. In: 2013 IEEE 13th International conference on data mining. IEEE, pp 647–656

  29. She Y, Owen AB (2011) Outlier detection using nonconvex penalized regression. J Am Stat Assoc 106(494):626–639

    Article  MathSciNet  Google Scholar 

  30. Skillicorn DB (2007) Detecting anomalies in graphs. In: 2007 IEEE Intelligence and Security Informatics. IEEE, pp. 209– 216

  31. Song X, Wu M, Jermaine C, Ranka S (2007) Conditional anomaly detection. IEEE Trans Knowl Data Eng 19(5):631–645

    Article  Google Scholar 

  32. Sun W, Shao S, Zhao R, Yan R, Zhang X, Chen X (2016) A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 89:171–178

    Article  Google Scholar 

  33. Xu X, Yuruk N, Feng Z, Schweiger TA (2007) Scan: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. pp 824–833

  34. Zhou M, Kong Y, Zhang S, Liu D, Jin H (2020) The deep fusion of topological structure and attribute information for link prediction. IEEE Access 8:34398–34406

    Article  Google Scholar 

  35. Zhu D, Ma Y, Liu Y (2020) Deepad: A joint embedding approach for anomaly detection on attributed networks. In: International conference on computational science. Springer, pp 294–307

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Acknowledgements

This work was supported in part by Zhejiang NSF Grant No. LZ20F020001 and No. LY20F020009, China NSF Grants No. 61602133,Ningbo NSF Grant No. 202003N4086,as well as programs sponsored by K.C. Wong Magna Fund in Ningbo University.

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  1. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China

    Jiangjun Su, Yihong Dong, Jiangbo Qian, Yu Xin & Jiacheng Pan

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  1. Jiangjun Su
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  2. Yihong Dong
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  3. Jiangbo Qian
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  4. Yu Xin
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  5. Jiacheng Pan
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Correspondence to Yihong Dong.

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Su, J., Dong, Y., Qian, J. et al. The deep fusion of topological structure and attribute information for anomaly detection in attributed networks. Appl Intell 52, 1013–1029 (2022). https://doi.org/10.1007/s10489-021-02386-3

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