UNSTRUCTURED
Iran has faced severe sanctions in recent years from some countries. Due to the dependence of the Iranian health industry on government payments, the health of the people in this country has suffered a lot. One of the solutions for Iran's sanctions to reduce the impact of sanctions on health is to rely on domestic researchers, but researchers in Iran are having problems. One way to reduce researchers' problems is to use the National Academic Social Network. This article describes the steps of setting up an academic social network in a developing country in four stages.
Abstract
Networking is not only essential for success in academia, but it should also be seen as a natural component of the scholarly profession. Research is typically not a purely individualistic enterprise. Academic social network sites give researchers the ability to publicise their research outputs and connect with each other. This work aims to investigate the use done by Italian scholars of 11/D2 scientific field. The picture presented shows a realistic insight into the Italian situation, although since the phenomenon is in rapid evolution results are not stable and generalizable.
The paper aims to discover overlapping communities and their research interests in academic social network. The network is constructed based on co-authorships. Cliques are extracted to discover overlapping communities. Keywords used by authors in communities are counted and sorted, and topNkeywords are selected to represent their research interests. The experimental results in the field of Information Resource Management testified the effectiveness of proposed method.
The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.
Presenting a dataset for collaborator recommending systems in academic social network
