Research Hotspots Mining and Visualized Analysis Based on Linking Cluster and K-Core Decomposition.

In this paper, we focus on research hotspots mining with knowledge semantic features on discipline topic word networks, which are extracted from scientific and technical documents. For the topic networks, a novel method of combining linking community clustering with k-core decomposition is proposed to mine the hotspot community with knowledge hierarchy structure. And as a post-processing, the hierarchical community identification is implemented by k-core decomposition in order to discover the internal connection with knowledge semantic hierarchy in the research hotspot. Finally, the community density and visualization method is utilized to analyze the core and sub- problem in the research hotspot community. In experiments, the topic networks for a single discipline and interdisciplinary research hotspot are taken as examples of analysis, and the results show the proposed approach can effectively realize the objective of mining and analyzing the discipline research hotspot on the academic topic word network.