Research on Improving the Semantic Main Path Analysis Method by Leveraging the Density Peak Clustering Algorithm
The semantic main path analysis(sMPA)method overcomes the shortcomings of the traditional main path anal-ysis(MPA)method,such as a single main path and low theme consistency.However,it also leaves two defects:the posi-tion of the selected main path in the semantic space may deviate from the cluster center,and the topic discrimination of dif-ferent main paths is not obvious.To address this problem,this study proposes a gradually optimized main path selection method in which topic cluster density and path traversal weight are superimposed to form a composite density,and the lo-cation of the topic cluster center is optimized by adjusting the proportion of the two elements in the composite density.When the cluster center converges,the paths located in different topic cluster centers are outputted.This method is verified by applying it to the patent citation network of lithium-ion batteries for electric vehicles and the citation network of high-impact papers in the field of materials science.The experimental results show that not only is the layout of multiple main paths generated by the new method but the possibility of selecting improper main paths is also significantly reduced.
semantic main path analysistheme consistencytopic clusteringmaterials scienceelectrical vehicle lithium-ion battery
NETL
NSTL
万方数据
维普
