Investigation and Analysis of Research Gate User’s Activities using Neural Networks

Online Social Networks (OSNs) have been proliferating in the past decade as general-purpose public networks. Billions of user's are currently subscribing by uploading, downloading, sharing opinions and blogging. Private OSNs emerged to tackle this issue. Research Gate (RG) is considered as one of the most popular private academic social networks for developers and researches in the internet. The current study consists of two parts. The first part is a measurement study of user's activities in RG and second part deals with the relationship between user's profile data and their links. To this end, a sample of one million RG user's records was. To facilitate this analysis, three layers back-propagation neural network models were generated. The purpose of this network is to show the correlation between user profiles data and the number of their followers. The results show that there is a high positive relationship between user's followers and research activities 'publication, impact factor, total number of publication views and citation'. In addition, the results indicated that the number of questions and answers (activity) of a user have low correlation with the corresponding followers. The present results demonstrate that the question/answer contributions of researchers are limited, which therefore, needs more collaboration from the RG researchers.