Nowadays web based tools are able to render a significant number of data from structured and no-structured databases. These web based tools are now amazing! They provide extraordinary results and are able to handle thousand of nodes in seconds. The main benefit of these tools is allowing users gathering immediately insights to produce actionable results.
We have the pleasure to play and work with Mira Analytics which is able to create compelling visualization about any topic, business application, sector starting from a specific datasets, as for instance, articles extracted from the scientific literature.
To fork the work done by Dahlander and Gann (2010)* we have prepared this graph based on more than 900 scientific papers talking about Open Innovation extracted from ISI web of Science, a database of scholars papers and articles owned by Thomson Reuters. After structuring the data into one file, I uploaded the dataset into MIRA Analytics (a tool developed from Miniera) and I was able to show the entire Ecosystem of Open Innovation scholars using a stunning network graph (see following image).
The authors and the relationships between them are visualized with nodes and edges respectively (red color). Additionally, (yellow dots) it is possible to see which are the main sources used for publishing the works. The tool has also the option to filter the data by time which helps identifying who is working in recent time or who has abandoned the ecosystem.
Moreover, if you are interested in one particular author (in this example), you can focus on it by searching or browsing and discover his/her personal network and what works were published (see image). It is also possible to see how strong is the network and if the author is persistently working with the same colleagues, for instance.
In conclusion, network graphs becomes very powerful when multiple concepts and metadata need to be analyzed together where a matrix of co-currency would not be able to show multiple relationship of more than two dimensions. Although this ecosystem was limited up to two dimensions. Depending form the type of data that is used and the algorithm that are applied allows you to discover phenomenas that would be quite impossible to see easily. It essentially enrich the analysis and it reduces tremendously the time of work.
(*) Dahlander, L. and D. M. Gann, (2010). “How open is innovation?”, Research Policy, 39(6): 699-709.
For more information see: https://youtu.be/vINkY5bEYvk)