Difference between revisions of "World Influence of Infectious Diseases from Wikipedia Network Analysis"
(+ links) |
(Infobox work) |
||
| Line 1: | Line 1: | ||
| + | {{Infobox work | ||
| + | | title = World Influence of Infectious Diseases from Wikipedia Network Analysis | ||
| + | | date = 2018 | ||
| + | | authors = [[Guillaume Rollin]]<br />[[J. Lages]]<br />[[Dima L. Shepelyansky]] | ||
| + | | doi = 10.1101/424465 | ||
| + | | link = https://www.biorxiv.org/content/early/2018/09/24/424465 | ||
| + | }} | ||
'''World Influence of Infectious Diseases from Wikipedia Network Analysis''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Guillaume Rollin]], [[J. Lages]] and [[Dima L. Shepelyansky]]. | '''World Influence of Infectious Diseases from Wikipedia Network Analysis''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Guillaume Rollin]], [[J. Lages]] and [[Dima L. Shepelyansky]]. | ||
== Overview == | == Overview == | ||
Authors consider the network of 5 416 537 articles of [[English Wikipedia]] extracted in 2017. Using the recent reduced [[Google]] matrix (REGOMAX) method authors construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix authors determine the sensitivity of world countries to specific diseases integrating their influence over all their history including the times of ancient Egyptian mummies. The obtained results are compared with the World Health Organization (WHO) data demonstrating that the [[Wikipedia]] network analysis provides reliable results with up to about 80 percent overlap between WHO and REGOMAX analyses. | Authors consider the network of 5 416 537 articles of [[English Wikipedia]] extracted in 2017. Using the recent reduced [[Google]] matrix (REGOMAX) method authors construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix authors determine the sensitivity of world countries to specific diseases integrating their influence over all their history including the times of ancient Egyptian mummies. The obtained results are compared with the World Health Organization (WHO) data demonstrating that the [[Wikipedia]] network analysis provides reliable results with up to about 80 percent overlap between WHO and REGOMAX analyses. | ||
Revision as of 11:22, 18 January 2020
Authors | Guillaume Rollin J. Lages Dima L. Shepelyansky |
|---|---|
Publication date | 2018 |
DOI | 10.1101/424465 |
Links | Original |
World Influence of Infectious Diseases from Wikipedia Network Analysis - scientific work related to Wikipedia quality published in 2018, written by Guillaume Rollin, J. Lages and Dima L. Shepelyansky.
Overview
Authors consider the network of 5 416 537 articles of English Wikipedia extracted in 2017. Using the recent reduced Google matrix (REGOMAX) method authors construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix authors determine the sensitivity of world countries to specific diseases integrating their influence over all their history including the times of ancient Egyptian mummies. The obtained results are compared with the World Health Organization (WHO) data demonstrating that the Wikipedia network analysis provides reliable results with up to about 80 percent overlap between WHO and REGOMAX analyses.
