A Correlative Scrutiny for Improving the Career Guidance Links in Social Network
Sudalai Muthu T1, Rohini A2
1SudalaiMuthu T*, Associate Professor, Department of Computer Science and Engineering, Hindustan Institute of Technology and Science, Chennai, India.
2Rohini A, Research Scholar, Department of Computer Science and Engineering, Hindustan Institute of Technology and Science, Chennai, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1466-1470 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1260109119/2019©BEIESP | DOI: 10.35940/ijeat.A1260.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Social Network analysis techniques have shown the ability to meet educators and influence career development guidance. Career education mentoring plays an important role in the career supporting which are the interest, the strength, and the aspirations of the students. In this paper, we proposed a career development framework enhances the node influence propagation and effective interaction between nodes is taken as a strong link, from the node influence propagation we divide it into two categories such as: (1) career predictions to persuade prospective graduates to pursue the desired career path, with career prospects considered by them as a learning opportunity. (2) Social network analysis and persuasive techniques are used to motivate within a social networking framework where there is a tendency to adopt desired professional behaviors. The process begins with the discovery of behavioral features to create a cognitive profile and to identify individual disabilities. We compare a clustering algorithm that predicts the accuracy values and pattern of creations to a social network for achieving collaborative cognition.
Keywords: Social Network, Community and Link Analysis.