Knowledge evolution in physics research: An analysis of bibliographic coupling networks

Abstract

Even as we advance the frontiers of physics knowledge, our understanding of how this knowledge evolves remains at the descriptive levels of Popper and Kuhn. Using the American Physical Society (APS) publications data sets, we ask in this paper how new knowledge is built upon old knowledge. We do so by constructing year-to-year bibliographic coupling networks, and identify in them validated communities that represent different research fields. We then visualize their evolutionary relationships in the form of alluvial diagrams, and show how they remain intact through APS journal splits. Quantitatively, we see that most fields undergo weak Popperian mixing, and it is rare for a field to remain isolated/undergo strong mixing. The sizes of fields obey a simple linear growth with recombination. We can also reliably predict the merging between two fields, but not for the considerably more complex splitting. Finally, we report a case study of two fields that underwent repeated merging and splitting around 1995, and how these Kuhnian events are correlated with breakthroughs on Bose-Einstein condensation (BEC), quantum teleportation, and slow light. This impact showed up quantitatively in the citations of the BEC field as a larger proportion of references from during and shortly after these events.

Community structure in co-inventor networks affects time to first citation for patents

“…Community structures in citation networks for scientific articles have been analyzed recently (see, for example, Refs [17][18][19]), and opportunities for similar studies on the available large amounts of rich patent-citation data are now also beginning to be realized [20,21]. Our study offers an example for the type of interesting insights that can be gained by applying network-analysis-based community-detection methods in the important area of technical innovation.…”

Predicting the Evolution of Physics Research from a Complex Network Perspective

“…The evolution of these problem driven groups is more or less completely documented by the papers published as outcomes of their research. By analyzing groups of closely related papers, researchers could extract rich information about knowledge processes [ 1 , 2 , 3 , 4 ]. The potential to map scientific progress using publication data has attracted enormous interest recently [ 5 , 6 , 7 ].…”

“…The potential to map scientific progress using publication data has attracted enormous interest recently [ 5 , 6 , 7 ]. However, compared to the study of science at the level of individual papers [ 8 , 9 , 10 ] and at the level of the whole citation network [ 11 , 12 , 13 , 14 , 15 ], where much work has already been done, the research on science at the community level is still limited [ 1 , 3 , 16 , 17 ].…”

Knowledge evolution : from the complex network perspective

“…In Chapter 2, we demonstrated the utility of visualizing and analysing scientific knowledge evolution for physics at the aggregated mesoscale through the use of alluvial diagrams [Liu et al, 2017]. In this picture, papers are clustered into groups (or communities) and these groups can grow or shrink, merge or split, new groups may arise while the others may dissolve.…”

“…We report a case study of two fields that underwent repeated merging and splitting in the 1990s and how these events produce large impacts on the TCs' reference distributions. The results in this chapter were published in [Liu et al, 2017] In Chapter 3, we study the knowledge evolution process from a different perspectivelanguage. It is well known that different research fields use different terminologies and like the reference, terminology also evolves with time.…”