Centrality and Flow Vergence gradient based Path analysis of scientific literature: A case study of Biotechnology for Engineering

Patterns in the growth and thematic evolution of Artificial Intelligence research: A study using Bradford distribution of productivity and Path Analysis

“…𝑊 𝐹𝑉 𝑖 is termed as the flow vergence index by Prabhakaran et al (2015) because positive value of the index can clearly indicate works that are flow divergent and negative value of the index indicate works that are flow convergent as shown in figure 2 (right). More details about theoretical origin and rationale behind the formulation of the FV index is already covered by Prabhakaran et al (2015), Lathabai et al (2015), Lathabai et al (2017), Lathabai et al (2018) and Prabhakaran et al (2018).…”

“…Another interesting thing about a knowledge flow in a citing-cited pair is that, existence of flow vergence potential for citing and cited work also implies the existence of difference or gradient in flow vergence potential between them. This potential difference between works i and j connected by an arc of citation from j to i can be termed as FV gradient (Lathabai et al, 2015) and it can be computed as:…”

“…However, in some special cases, when newer works (citing works) tend to perform better than the former ones (cited ones) due to its inherent merit or innovative appeal, arcs having ∆𝐹𝑉 𝑖𝑗 < 0 can be found, making knowledge flow to appear as to have occurred from low potential work to a high potential work. This phenomenon was termed as Flow Vergence effect or FV effect and was used to detect (Lathabai et al, 2015) and predict (Prabhakaran et al, 2018) pivot papers of paradigm shift. Computation of FV gradients of all the arcs makes the network a signed weighted network and therefore FV gradient computation can be treated as a weight assignment method that probably have the ability to highlight paths that might not be highlighted through SPX methods as shown in Lathabai et al (2018).…”

“…It works upon unweighted citation networks after (i) weight assignment through diligent methods (such as traversal -based weight assignment schemes) and (ii) tracing the connective threads of knowledge through search schemes. Some of the major weight assignment schemes include (i) the schemes like SPLC, SPNP (Hummon & Doreian, 1989) and SPC or Search Path Count (Batagelj, 2003), etc., that are collectively referred to as SPX methods and (ii) Flow Vergence gradient or FV gradient (Prabhakaran et al, 2015;Lathabai et al, 2015) weight assignment scheme. Different search schemes that can be used to retrieve evolutionary trajectories by tracing through the weighted networks are (i) forward (local) search, (ii) global standard search or critical path method (Hummon & Doreian, 1989), (iii) backward (local) search, (iv) key-route (local) search, (v) key-route (global) search (Liu & Lu, 2012).…”

“…For this the study proposed three indicesweights of arcs that are proportional to the number of different source-sink paths passing through the arc. Similarly, other researchers also presented some novel search schemes and weight assignment methods (Batagelj, 2003;Prabhakaran et al, 2015;Lathabai et al, 2015;Liu & Lu, 2012). Some of the previous studies have utilized these weight assignment and search schemes to get the evolutionary trajectories of knowledge flows in the citation network.…”