Intelligent Model for Research Peer Recommendation

  • Vanitha Sivagami S, Abirami Sri S

Abstract

Social networking provides the ability to connect to other people all over the world.  It can be represented by the graph in which nodes represent people and edges are used to show the connection between them. The tendency of people with the similar taste or choices in a social network leads to the formation of communities. Community detection in a social network is becoming a hot topic now-a-days since it is useful for advertising and marketing purposes. None of the existing community retrieval algorithm provides a real time view of the problem .i.e) It does not recommend the optimal peers to the user. The objective of this paper is to develop the efficient community retrieval algorithm that will recommend the optimal research peers to the new user who is looking for collaboration. Initially, research peer information is converted into the form of attributed graph. In order to make searching efficient Core Label(CL) tree is constructed based on the core numbers of vertices of the graph. By using an efficient community retrieval algorithm, the optimal communities are retrieved and recommended to the user. If the retrieved community is not of suitable size then keyword set expansion takes place and searching process is repeated again. This will be continued until the community of suitable size is retrieved. The experimental results show that our proposed model performs well even for large size graph

Published
2020-04-20
How to Cite
Vanitha Sivagami S, Abirami Sri S. (2020). Intelligent Model for Research Peer Recommendation. International Journal of Advanced Science and Technology, 29(7s), 1721 - 1734. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/11063