Abstract
In recent years, the usage of academic social network sites (ASNS) has been increased extensively for research-oriented activities. The accessibility, flexibility and availability of digitized information present on diverse ASNS platforms offer researchers the possibilities to boost their visibility to mark an impact in global research community. Amidst other ASNS, ResearchGate (RG) is prevalently exploited. Information shared on RG is categorized into user demographics and user associations. This structured organization of information makes RG comparable to social media platforms such as Twitter, Facebook and LinkedIn. Such social media platforms are widely examined as connected networks in various applications. To model RG information into network, this research proposes a novel hierarchical data rendering process to collect the linked RG information. Correspondingly, this research proposes a novel framework, RGNet, to model RG information into network including user demographics and user associations, implementing the proposed hierarchical data rendering process. The achieved outcomes reveal that the linked RG information can be precisely represented, explored and analysed leveraging the concepts of network modelling and network theory analogous to various impactful applications of social network analysis. Further, the technological advancements provide various competent mechanisms for network storage and network information retrieval for constructed RG network. The graphical visualization of the constructed RG network is also presented in this research.
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Desai, M., Mehta, R.G., Rana, D.P. (2021). RGNet: The Novel Framework to Model Linked ResearchGate Information into Network Using Hierarchical Data Rendering. In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-5243-4_4
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DOI: https://doi.org/10.1007/978-981-15-5243-4_4
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