International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 47 | Number 1 | Year 2017 | Article Id. IJCTT-V47P116 | DOI : https://doi.org/10.14445/22312803/IJCTT-V47P116

Review of different types of Anomalies and Anomaly detection techniques in Social Networks based on Graphs

Sarbjeet kaur, Prabhjot Kaur

Citation :

Sarbjeet kaur, Prabhjot Kaur, "Review of different types of Anomalies and Anomaly detection techniques in Social Networks based on Graphs," International Journal of Computer Trends and Technology (IJCTT), vol. 47, no. 1, pp. 116-121, 2017. Crossref, https://doi.org/10.14445/22312803/IJCTT-V47P116

Abstract

As today is a trend of social networking to communicate with each other, there is a possibility of anomalous users in online networks to steal other’s personal information etc. It is necessary to understand the behavior of different users to find fake and genuine users from networks. To find anomalies in network we should understand social network analysis, different type of anomalies and different social network metrics. In this paper we have reviewed different type of social network metrics, types of anomalies and anomaly detection techniques based on graphs. This paper will help to understand social networking, social network metrics, anomaly types and anomaly detection techniques to find anomalous users from online social networks.

Keywords

Anomaly, Anomaly detection, Metrics.

References

1. G. E. Hinton and S. T. Roweis,” Stochastic neighbor embedding”, 2002, NIPS
2. J. Huang, H. Sun, J. Han, H. Deng, Y. Sun, and Y. Liu,” SHRINK: a structural clustering algorithm for detecting hierarchical communities in networks”, 2010, CIKM
3. G. Karypis and V. Kumar,” A fast and high quality multilevel scheme for partitioning irregular graphs”, 1998, SIAM J. Sci. Comput., 20(1):359–392.
4. A. Lancichinetti, S. Fortunato, and F. Radicchi,” Benchmark graphs for testing community detection algorithms”, 2008, Phys. Rev. E, 78(4):046110
5. T. Lou and J. Tang,” Mining structural hole spanners through information diffusion in social networks”, 2013, WWW
6. A. Agovic, A. Banerjee, A. R. Ganguly, and V. Protopopescu,” Anomaly detection using manifold embedding and its applications in transportation corridors”, 2009, Intelligent Data Anal., 13(3):435–455,
7. L. Akoglu, H. Tong, and D. Koutra,” Graph based anomaly detection and description: a survey”, 2015, Data Min. Knowl. Discov., 29(3):626–688
8. L. Armijo,” Minimization of functions having lipschitz continuous first partial derivatives”, 1966, Pacific J. Math, 16(1):1–3,
9. P. Bogdanov, C. Faloutsos, M. Mongiovi, E. E. Papalexakis, R. Ranca, and A. K. Singh,” Netspot: Spotting significant anomalous regions on dynamic networks”, 2013, SDM
10. Hodge, Victoria J., and Jim Austin. "A survey of outlier detection methodologies." Artificial Intelligence Review 22.2 (2004): 85-126.
11. Ravneet kaur, Sarbjeet singh,” A survey of data mining”, Egyptian Informatics Journal(2016)17,199-216.