Dr. S. Karthika

Associate Professor

Dr. S. Karthika M.E., Ph.D

Email skarthika@ssn.edu.in

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Dr. S. Karthika, Associate Professor in the Department of Information Technology has over   9 years of teaching and 2 years of exclusive research experience in Social Networking and Machine Learning.


She received B.E (CSE) degree from Avinashilingam University and completed her M.E. (CSE) from Anna University. She has earned her Ph.D. degree under the Faculty of Information and Communication Engineering from Anna University.

During her Ph.D., she developed a system for key player identification using behavioural analysis in covert networks. The system used novel centrality based outlier analysis algorithm to recognize the influencers, and analysed the fragmentation and resilience of the covert network using graph theoretic approach. She carried her research work as Junior Research Fellow under UGC-BSR fellowship.

She is a member of professional societies, including IEEE and ISTE. She is the nodal co-ordinator of SSN Resource Centre under Nation Cyber Security Programme – NCSP, NCDRC. She is SSN-IEEE WIE Affinity group Student Branch Counselor.


Her areas of research include key player analysis, text mining, privacy in online social network, rumour analysis, opinion mining and Social IoT. To her credit she has 10 refereed journals and 15 international conference publications.

She is a recognized supervisor of Anna University and currently guiding two fulltime research scholars. She also has a team of U.G and P.G students working in the area of SNA and cyber security.

As PI she has received two faculty funded projects from SSN Trust worth 5.25 Lakhs. In the first project she analysed the covert network structure and node attribute information to improve performance of both the link prediction and the attribute inference problems. Presently, she is executing her second project in Twitter analysis. This project analyses the behaviour of the user based on the sensitivity of the posted tweets.


She follows an activity and outcome based teaching methodology in the subjects like Database, Operating System and Data Warehousing and Data Mining.

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