Recently, there was an article in Scientometrics about main path analysis by Liu et al. It’s supposed to help trace the development path of a scientific or technological field. Before hearing this, I was just being content with the capabilities of CitNetExplorer in showing the trends in my field of interest. However, after reading the technique’s capabilities. I was quite intrigued as it may make analyzing the overarching trend in a field of interest simpler to visualize. The only problem is that there is really no tutorial on how to do it. The only thing I found was this youtube video using Pajek, which honestly was not very informative. To add to that, I did not have experience with Pajek, and with its very intimidating interface, I really had to tinker with it. Nonetheless, after playing with it, I hacked my way into generating my own main path analysis plots.
In the following, I will explain the process. Note that I do not have much experience with Pajek so there might be easier ways to do it.
The workflow I engineered was this (more explanation in the coming days):
- Download articles from Web of Knowledge
- Import articles to CitNetExplorer
- Export the citation network file from CitNetExplorer
- Reformat the file into a Pajek .net file
- Import Pajek net file to Pajek
- Run Network -> Acyclic Network -> Create Weighted Network + Vector -> Trasversal Weights -> Search Path Link Count (SPLC). Note that you can choose others weights such as SPC and SPNP. In the article above however, they recommended SPLC as they said that it somehow reflects how knowledge diffuse in real life.
- Run Network -> Acyclic Network -> Create (Sub)Network -> Main Paths -> Global Search -> Key-Route
- Enter an arbitrary number of routes. I tried 1-50.
- Run Draw -> Network
- Run Layout -> Kamada Kawai -> Fix first and last vertex
This is a sample map for the field of Fragment-based drug discovery.
[In progress. Updates in the coming days]