Detection of Leading Experts from ResearchGate
This article describes how thanks to the technological development, social media has propagated in recent years. The latter describes a range of Web-based platforms that enable people to socially interact with one another online. Several types of social media appeared. In this context, the author focuses on scientific social network which connects the researchers and allow them to communicate and collaborate online. In this paper, we, particularly, aim to detect the scientific leaders through firstly detect communities in social network then identify the leader of each group. To do this, the author introduces a new hierarchical semi-supervised clustering method based on ordinal density. The results of carried out experiments on real scientific warehouse have shown significant profits in terms of accuracy and performance.
Year of publication: |
2018
|
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Authors: | Ben Ahmed, Eya |
Published in: |
International Journal of Business Analytics (IJBAN). - IGI Global, ISSN 2334-4555, ZDB-ID 2797982-9. - Vol. 5.2018, 3 (01.07.), p. 67-86
|
Publisher: |
IGI Global |
Subject: | Clustering | Data Warehouse | Group Detection | Hierarchical Semi- Supervised Clustering | Leader | Researcher | Scientific Network Warehousing | Scientific Social Network | Social Network |
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