TY - JOUR
T1 - Co-citations in context
T2 - Disciplinary heterogeneity is relevant
AU - Bradley, James
AU - Devarakonda, Sitaram
AU - Davey, Avon
AU - Korobskiy, Dmitriy
AU - Liu, Siyu
AU - Lakhdar-Hamina, Djamil
AU - Warnow, Tandy
AU - Chacko, George
N1 - Funding Information:
Research and development reported in this publication was partially supported by federal funds from the National Institute on Drug Abuse, National Institutes of Health, U.S. Department of Health and Human Services, under Contract Nos. HHSN271201700053C (N43DA-17-1216) and HHSN271201800040C (N44DA-18-1216). The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Tandy Warnow receives funding from the Grainger Foundation.
Publisher Copyright:
© 2019 James Bradley, Sitaram Devarakonda, Avon Davey, Dmitriy Korobskiy, Siyu Liu, Djamil Lakhdar-Hamina, Tandy Warnow and George Chacko.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - Citation analysis of the scientific literature has been used to study and define disciplinary boundaries, to trace the dissemination of knowledge, and to estimate impact. Co-citation, the frequency with which pairs of publications are cited, provides insight into how documents relate to each other and across fields. Co-citation analysis has been used to characterize combinations of prior work as conventional or innovative and to derive features of highly cited publications. Given the organization of science into disciplines, a key question is the sensitivity of such analyses to frame of reference. Our study examines this question using semantically themed citation networks. We observe that trends reported to be true across the scientific literature do not hold for focused citation networks, and we conclude that inferring novelty using co-citation analysis and random graph models benefits from disciplinary context.
AB - Citation analysis of the scientific literature has been used to study and define disciplinary boundaries, to trace the dissemination of knowledge, and to estimate impact. Co-citation, the frequency with which pairs of publications are cited, provides insight into how documents relate to each other and across fields. Co-citation analysis has been used to characterize combinations of prior work as conventional or innovative and to derive features of highly cited publications. Given the organization of science into disciplines, a key question is the sensitivity of such analyses to frame of reference. Our study examines this question using semantically themed citation networks. We observe that trends reported to be true across the scientific literature do not hold for focused citation networks, and we conclude that inferring novelty using co-citation analysis and random graph models benefits from disciplinary context.
KW - Bibliometrics
KW - Co-citation analysis
KW - Random graphs
UR - http://www.scopus.com/inward/record.url?scp=85117784020&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85117784020&partnerID=8YFLogxK
U2 - 10.1162/qss_a_00007
DO - 10.1162/qss_a_00007
M3 - Article
AN - SCOPUS:85117784020
VL - 1
SP - 264
EP - 276
JO - Quantitative Science Studies
JF - Quantitative Science Studies
SN - 2641-3337
IS - 1
ER -