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authorship_parse.py generates the authorship graph from the abstract files
authorship_properties.py calculates some properties of the actual authorship graph
authorship_properties2.py calculates the macro properties of the actual authorship graph
authorship_properties2_compare.py compares the macro properties of our growth model with the actual graph
authorship_simulate.py runs our citation network growth model
Citations
citations_parse.py extracts useful data out of the citation graph
citations_properties.py calculates properties of the actual citation graph
In the data folder...
random.txt
Average activity level over all authors. Average activity level is given by number of collaborations an author makes per year. Calculated by summing the total number of collaborations per author, then dividing by the number of "active" years an author has (first year of collaboration subtracted from 2003)
/graphs
activity_distribution is the distribution of # of collaborations per author over the whole dataset
citation_distribution is the distribution of # of citations per paper over the whole dataset
new_authors_per_year shows the # of new authors that appear each year
papers_per_author shows distribution # of papers each author wrote over the whole dataset (includes papers written without collaborators)
s_activity_level looks at the number of collaborations that authors with a certain range of total collaborations have each year
s_citation_level looks at the distribution of # of citations a paper receives per year from the year it got published (for various ranges of # of citations received)
/parsed
papers_citations? is the dictionary of years in which a paper got cited
papers_cpapers? is the list of papers that a specific paper cited
citations? is the number of citations a specific paper got
Prefixes
g = Gamma Distribution with a = 3, b = 2
dg = Gamma Distribution with a = degree of node, b = 2
drg = Gamma Distribution with a = sqrt(degree of node), b = 2