Community Identity in Astrophysics
收藏Figshare2019-01-18 更新2026-04-29 收录
下载链接:
https://figshare.com/articles/dataset/Community_Identity_in_Astrophysics/7547696
下载链接
链接失效反馈官方服务:
资源简介:
Communities of the Dark Matter citation network [Duffee, 2018, p97] visualised with Gephi's Force Atlas algorithm, showing the overlap of the citation communities. This network consisted of 47 187 nodes and 161 870 links created from citations to and by 2659 papers on the subject of Dark Matter or MOND (MOdified Newtonian Dynamics, an alternative theory to Dark Matter), lodged in arXiv between 1992 and 2009.The nodes are coloured according to the largest seven communities found by the Louvain algorithm with, in decreasing order of size:- purple traditional astrophysics (11.57%),- green high-energy physics (11.11%),- blue cosmology and gravitational lensing (10.92%),- red Dark Energy and alternate theories (10.45%),- black detection of particles and high-energy physics (8.15%),- orange Dark Matter models and galaxy clusters (8.15%),- turquoise MOND, an alternate theory to Dark Matter (7.53%)Network statistics:modularity 0.617average degree 3.43average clustering co-efficient 0.067The top seven communities were labelled by examining the titles of the papers in the community and the label was chosen to best represent the character of the majority of the titles. The layout is determined by the citations between papers and it is intuitive to find related areas adjacent to each other, particle physics next to high-energy physics and cosmology close to astrophysics. This layout places high-energy physics between particle physics and astrophysics,a good location for theoretical work connecting astronomical observation with laboratory experiment. It also places MOND, one of several alternative theories, as overlappingtraditional astrophysics and Dark Energy + alternative theories. The separation of galaxy clusters and Dark Matter models from astrophysics could be due to a strongly self-contained research community but would need more investigation to verify that claim.reference:Duffee, Boyd (2018) 'Quantifying textual similarities across scientific research communities' Doctoral thesis, Keele University.
创建时间:
2019-01-18



