Kim, M.; Baek, I.; Song, M.: Topic diffusion analysis of a weighted citation network in biomedical literature (2018)
0.00
0.0017656671 = product of:
0.0070626684 = sum of:
0.0070626684 = product of:
0.028250674 = sum of:
0.028250674 = weight(_text_:based in 4036) [ClassicSimilarity], result of:
0.028250674 = score(doc=4036,freq=2.0), product of:
0.14144066 = queryWeight, product of:
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.04694356 = queryNorm
0.19973516 = fieldWeight in 4036, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.0129938 = idf(docFreq=5906, maxDocs=44218)
0.046875 = fieldNorm(doc=4036)
0.25 = coord(1/4)
0.25 = coord(1/4)
- Abstract
- In this study, we propose a framework for detecting topic evolutions in weighted citation networks. Citation networks are important in studying knowledge flows; however, citation network analysis has primarily focused on binary networks in which the individual citation influences of each cited paper in a citing paper are considered identical, even though not all cited papers have a significant influence on the cited publication. Accordingly, it is necessary to build and analyze a citation network comprising scholarly publications that notably impact one another, thus identifying topic evolution in a more precise manner. To measure the strength of citation influence and identify paper topics, we employ a citation influence topic model primarily based on topical inheritance between cited and citing papers. Using scholarly publications in the field of the protein p53 as a case study, we build a citation network, filter it using citation influence values, and examine the diffusion of topics not only in the field but also in the subfields of p53.
