A Novel Research Topic Ranking System in Academic Networks

Abstract

In today world, various types of communication networks such as academic, social, technological, business and etc. come into front. All of the networks are continuously growing and expanding in volume, velocity and variety like popular platform Big Data. Among them, the academic networks such as Alumni, Research Gate, Student Network, Teacher Network and so on provide a powerful abstraction of the academic structure and dynamics of diverse kinds of inter personal academic activities and interaction. Generally, the academic network contents such as research findings and educational concepts are created and consumed by the influences of all different academic navigation paths that lead to the challenging research issues. Therefore, identifying important and researcher relevant refined structures such as new research topics information or academic communities become major factors in modern decision making world.. In this paper, we propose a novel research topic ranking system in academic networks by using the research data relational graphs from academic media platform jointly with educational data to improve the relevance between research topics and researchers intentions (i.e., academic relevance). Specifically, we propose a stochastic model based Academic-Research Topic Ranking algorithm by taking academic value into account. By using some extensive experiments, we demonstrated the significant and effectiveness of the proposed academic-research topic ranking method.