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collab_divers.py
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collab_divers.py
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#!/usr/bin/env python
import pandas as pd
import networkx as nx
from networkx.algorithms import bipartite
import math
bnodes = list(pd.read_csv('path/to/input.csv')['bottom'])
tnodes = list(pd.read_csv('path/to/input.csv')['top'])
# CREATING BIPARTITE NETWORK
def create_bip_network(bnodes,tnodes):
B = nx.Graph()
for i in range(len(bnodes)):
B.add_node(tnodes[i], bipartite=0)
B.add_node(bnodes[i], bipartite=1)
B.add_edge(tnodes[i], bnodes[i])
return B
# CALCULATING COLLABORATIVENESS AND DIVERSITY
def collaborativeness(B):
# splitting the types of nodes of the graph object B
top_nodes = set(node for node,d in B.nodes(data=True) if d['bipartite']==0) #set of top nodes
bottom_nodes = set(B) - top_nodes #set of bottom nodes
deg_top, deg_bottom = bipartite.degrees(B,bottom_nodes) #dictionary: nodes as keys, degrees as values
# creating simple graph and multigraph bottom projections
G = bipartite.projected_graph(B,bottom_nodes)
Gm = bipartite.projected_graph(B,bottom_nodes,multigraph=True)
col_dict = {}
#ratio_dict = {}
#div_dict = {}
for node in bottom_nodes:
if G.degree(node) > 0:
gamma = 0
shared = 0
for nbr in B[node]:
gamma += math.log(B.degree(nbr))
if B.degree(nbr) > 1:
shared += 1
col_dict[node] = ((float(shared)/B.degree(node))*gamma, float(G.degree(node))/Gm.degree(node))
#ratio_dict[node] = (float(shared)/B.degree(node))
#diversity_dict[node] = float(G.degree(node))/Gm.degree(node)
return col_dict
B = create_bip_network(bnodes,tnodes)
col_div = collaborativeness(B)
with open('path/to/output.csv', 'w') as g:
g.write('node,collaborativeness,diversity')
g.write('\n')
for node in list(set(bnodes)):
g.write(node + ',' + str(col_div[node][0]) + ',' + str(col_div[node][1]))
g.write('\n')