A Novel Ranking Model for a Large-Scale Scientific Publication

With a large number of scientific literature, it has been difficult to search for a set of relevant articles and to rank them. In this work, we propose a generalized network analysis approach (called N-star ranking model) for sorting them based on . The ranking of the result is considered in the mutual relationships between another classes: keyword, publication, citation. From the model, we propose two ranks for this problem: the Universal-Publication rank - (UP rank) and Topic-Publication rank (TP rank). We also study two simple ranks based on citation counting (RCC rank) and content matching (RCM rank). We propose the metrics for ranking comparison and analysis on two criteria value and order. We have conducted the experimentations for confirming the predictions and studying the features of the ranks. The results show that the proposed ranks are very impressive for the given problem since they consider the query/topic, the content of publication and the citations in the ranking model.