Research Front Detection and Topic Evolution Based on Topological Structure and the PageRank Algorithm

Abstract

Research front detection and topic evolution has for a long time been an important direction for research in the informetrics field. However, most previous studies either simply use a citation count for scientific document clustering or assume that each scientific document has the same importance in detecting the clustering theme in a cluster. In this study, utilizing the topological structure and the PageRank algorithm, we propose a new research front detection and topic evolution approach based on graph theory. This approach is made up of three stages: (1) Setting a time window with appropriate length according to the accuracy of scientific documents clustering results and the time delay of a scientific document to be cited, dividing scientific documents into several time windows according to their years of publication, calculating similarities between them according to their topological structure, and clustering them in each time window based on the fast greedy algorithm; (2) combining the PageRank algorithm and keywords’ frequency to detect the clustering theme, which assumes that the more important a scientific document in the cluster is, the greater the possibility that it is cited by the other documents in the same cluster; and (3) reconstructing the cluster graph where nodes represent clusters and edges’ strengths represent the similarities between different clusters, then detecting research front and identifying topic evolution based on the reconstructed cluster graph. To evaluate the performance of our proposed approach, the scientific documents related to data mining and covered by Science Citation Index Expanded (SCI-EXPANDED) or Social Science Citation Index (SSCI) in Web of Science are collected as a case study. The experiment’s results show that the proposed approach can obtain reasonable clustering results, and it is effective for research front detection and topic evolution.

RETRACTED ARTICLE: Research on Hot Topic Recognition and Its Evolution Analysis Method Based on LDA

“…Bai et al [4] conducted a coword analysis on keywords in the field of e-learning and accurately detected bridges, popularity, and core topics in the two periods of research. Xu et al [5] used topological structure and PageRank algorithm to propose a new research frontier detection and topic evolution method based on graph theory. Although the research on keyword clustering analysis has become more and more mature, in the actual research process, the results of clustering analysis are often not very satisfactory.…”

Technology Hotspot Tracking: Topic Discovery and Evolution of China’s Blockchain Patents Based on a Dynamic LDA Model

“…Overcomes the limitations of keyword-based network [13] Monitors the evolution of author interest [26] Graph-based theory Research front detection and topic evolution [27] Computational content analysis CSR-related conversations in the Twitter-sphere [28]Functional count data model The patent data of Applecompany…”

“…Yang proposed an ordering-sensitive and semantic-aware dynamic author topic model that monitors the evolution of author interest in timestamped documents [25]. Xu proposed a new research front detection and topic evolution approach based on graph theory utilizing topological structure and the PageRank algorithm [26]. Chae used computational content analysis to understand topics from CSR-related conversations in the Twitter-sphere to find directions for future research [27].…”

Comparative Analysis of Ranking Algorithms Used On Web

“…[16] Suppose at any point problem arises, then at that point records are extricated and ranking on the basis of the rank scores. In this calculation, it is the mixture of multiple models of comparable preparing inquiries [19]. Exploratory outcomes show that the question subordinate positioning calculation is superior to different calculations.…”

“…• Normalization: Normalization of the considerable number of centre points and leader/authority value is standardized through isolation by the square base of aggregate of all values of centre point and all values of power individually [19].…”