Main Path Identification Involving Article'S Associated Attribute: A Case Study Of Synthetic Biology

This paper proposes a new measure to trace the main paths of knowledge flows, which are characteristics of particular semantics and can cater to different research or application demands The traditionally main path analysis usually neglects the inequivalence between citations (H&D, 1989), leading to inaccurate results. To address this problem, we take the documents' associated attributes into account to measure the inequivalence, and further transform the inequivalence into a relevant relationship. Our method is a modification based on SPC from Batagelj (2003), using meta-path to describe and quantify the relevancy on the basis of associated attribute's correlation, then incorporating the relevancy into the link's traversal weight, and finally combining it with original SPC traversal weight to form a modified index. Synthetic biology is taken as a case study to test the performance of the new index and the results proved that our method can be an effective complement to main path analysis. Our method mainly boasts two beneficial effects: (1) it further developed the methodology of main path identification. A heterogeneous bibliometric network based on citations was constructed, and meta-paths were used to enrich main paths with more information and semantics; (2) the application scenario of main path analysis was expanded. Selected associated attributes can be integrated into main paths, catering to different research or application demands.