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Quantifying the evolution of citation cascades
Author(s) -
Min Chao,
Sun Jianjun,
Ding Ying
Publication year - 2017
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2017.14505401147
Subject(s) - citation , cascade , construct (python library) , data science , range (aeronautics) , citation analysis , perspective (graphical) , graph , computer science , theoretical computer science , artificial intelligence , chemistry , library science , engineering , chromatography , programming language , aerospace engineering
Citation is an important measurement in science which provides valuable clues to the historical development and trend forecast of science. However, the evolution of citation structure remains poorly understood despite long‐period and frequent use of citation counts for assessment purposes within scientific community. We observe citation structure from a cascade perspective and construct a large and long‐range citation graph based on real data. Two metrics are used to quantify the structural virality and cascade size. Preliminary results show that (1) age plays an important role for the evolution of citation cascades; (2) average depth tends to be influenced by both lifespan and the whole volume of scientific literature and grows much slower than cascade size; (3) there exists a stable positive relationship between average depth and cascade size, and it seems that an ultimate status exists where most papers eventually have large values for both metrics.