def coauthor_energy():
""" Creates clean graph of high energy physics collaborations in ArchiveX
Cleaned to produce a graph with 8480 nodes and 113600 edges.
During the cleaning process removed 3528 nodes with average degree 1.38.
"""
egraph = load_graph(ENERGY_COAUTHOR, format_2)
CD.clean_fragments(egraph, 20)
CD.clean_coauthor_network(egraph, 3) # keep nodes of degree 3 or higher
prefix = gen_prefix("High Energy Physics", 8480, 113600)
CD.write_edges(egraph,
DATA_PATH_2+"CollaborationNetworks/cleaned-energy.txt",
prefix)
return egraph
def coauthor_cond():
""" Creates Clean graph of condensed matter ArchiveX collaborations
Cleaned to produce a graph with 16966 nodes and 84747 edges.
During the cleaning process removed 3528 nodes with average degree 1.41.
"""
cgraph = load_graph(COND_COAUTHOR, format_2)
CD.clean_fragments(cgraph, 30)
CD.clean_coauthor_network(cgraph, 3)
prefix = gen_prefix("Condensed Matter", 16966, 84747)
CD.write_edges(cgraph,
DATA_PATH_2+"CollaborationNetworks/cleaned-cond.txt",
prefix)
return cgraph
def physics_citations():
"""Creates the graph papers citing other papers.
Requires
--------
SNAP formulated data file: www.snap.stanford.edu/data/index.html
Returns
-------
G : a NetworkX graph, where each node is a paper and edges indicate
a citation within the paper to another paper
"""
pgraph = load_graph(PHYSICS_CITATIONS, format_1)
CD.clean_fragments(pgraph, 15)
return pgraph
def coauthor_astro():
""" Creates Clean graph of astrophysics ArchiveX collaborations.
This is the hardest clean - well connected.
Cleaned to produce a graph with 15507 nodes and 193396 edges.
During the cleaning process removed 3268 nodes with average degree 1.43.
"""
agraph = load_graph(ASTRO_COAUTHOR, format_2)
CD.clean_fragments(agraph, 20)
CD.clean_coauthor_network(agraph, 3)
prefix = gen_prefix("Astrophysics", 15507, 193396)
CD.write_edges(agraph,
DATA_PATH_2+"CollaborationNetworks/cleaned-astro.txt",
prefix)
return agraph
def coauthor_energy_theory():
""" Creates clean graph pf high energy theory physics collaborations in
ArchiveX.
Note
----
There is a discrepancy in the number of edges that actually exist and the
number claimed on the SNAP website. There are multiple copies and self
loops in the graph. We clean those out to get 25998 edges.
With additional cleaning we take the graph from 9877 nodes to 5446 nodes
and 20337 edges. So the average degree of the nodes removed is 1.35
"""
egraph = load_graph(ENERGY_THEORY_COAUTHOR, format_2)
CD.clean_fragments(egraph, 30)
CD.clean_coauthor_network(egraph, 3) # keep nodes of degree 3 or higher
prefix = gen_prefix("High Energy Theory Physics", 5446, 20337)
CD.write_edges(egraph,
DATA_PATH_2+"CollaborationNetworks/cleaned-energy-theory.txt",
prefix)
return egraph
def coauthor_relativity():
""" General Relativity coauthor graph.
Note
----
Same discrepancy from self-loops and duplicate edges.
The graph begins with 5242 nodes and 14484 edges.
The graph returns is cleaned of 2548 nodes (nodes that are disconnected
from the main component, or not that well connected to the big component)
The resultant graph has 2694 nodes and 11400 edges. The average degree of
nodes removed is ~1.21
"""
rgraph = load_graph(RELATIVITY_COAUTHOR, format_2)
CD.clean_fragments(rgraph, 20)
CD.clean_coauthor_network(rgraph, 3) # keep nodes of degree 3 or higher
prefix = gen_prefix("General Relativity", 2694, 11400)
CD.write_edges(rgraph,
DATA_PATH_2+"CollaborationNetworks/cleaned-relativity.txt",
prefix)
return rgraph
