Review of different types of Anomalies and Anomaly detection techniques in Social Networks based on Graphs
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) V47(2):116-121, May 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
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.
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.
Keywords
Anomaly, Anomaly detection, Metrics.