Abstract
The understanding of references in research articles is essential for performing effectual research. This paper devises a hybrid model to find the influential cited paper and influential researchers from Web of Science (WOS) data. For determining the influential researcher, a series of steps is performed. Then the co-citation is performed for providing author-author co-relation that predicts the next co-author. Thereafter, visualization of the network is performed for research communication amongst different authors. Then, the network density is computed. Finally, the cluster coefficient is adapted for finding the influential researcher. Concurrently, for discovering influential cited papers, the pre-processing is performed using the stop word removal and stemming process. Then, the word2vec model is utilized for training the model to forecast the suitable word that comes next. Finally, the modified word mover's distance (MWMD) is utilized for determining the semantic similarity in order to discover influential cited papers.
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