# Density is computed on binarized matrix
FF_bin_density = nx.density(FF)
# Transitivity
FF_bin_transitivity = nx.transitivity(FF)
# Reciprocity
FF_reciprocity = hp.reciprocity(FF)
# Weakly connected components
# FF_partition = nx.weakly_connected_components(FF)[0]
# FF_comp = FF.subgraph(FF_partition)
# Clustering
FF_clustering = nx.average_clustering(FF.to_undirected(reciprocal=True))
# To undirected only keeps edges that are reciprocal
# Average path length
FF_avg_path_length = nx.average_shortest_path_length(FF)
# Avg Tie strength
FF_avg_volume = hp.average_tie_strength(FF)
# TODO:
# What about network centralization ?
# Test the output of NetworkX against UCINET
if names[i] == "a":
print "######################## GROUP MEASURES TEST #####################"
# Test for BINARY
if nx.density(FF) == nx.density(FF_bin): print "NOTICE: Group Density is BINARY"
if nx.transitivity(FF) == nx.transitivity(FF_bin): print "NOTICE: Group Transitivity is BINARY"
if hp.reciprocity(FF) == hp.reciprocity(FF_bin): print "NOTICE: Group Reciprocity is BINARY"
if nx.average_clustering(FF.to_undirected()) == nx.average_clustering(FF_bin.to_undirected()): print "NOTICE: Average Clustering is BINARY and UNDIRECTED"
if nx.average_shortest_path_length(FF) == nx.average_shortest_path_length(FF_bin): print "NOTICE: Group Average path length is is BINARY"
if hp.average_tie_strength(FF) == hp.average_tie_strength(FF_bin): print "NOTICE: Group Average tie strength path is is BINARY"
def main(argv):
#Standardvalues
partitionfile = "data/partitions/final_partitions_p100_200_0.2.csv"
project = "584"
to_pajek = True
try:
opts, args = getopt.getopt(argv,"p:s:o")
except getopt.GetoptError:
print 'group_bonding.py -p <project_name> -s <partitionfile> -o [if you want pajek output]'
sys.exit(2)
for opt, arg in opts:
if opt in ("-p"):
project = arg
elif opt in ("-s"):
partitionfile = arg
elif opt in ("-o"):
to_pajek = True
else:
print 'group_bonding.py -p <project_name> -s <partitionfile> -o [if you want pajek output]'
print "##################### GROUP BONDING ########################"
print "Project %s " % project
print "Partition %s" % partitionfile
csv_writer = csv.writer(open('results/spss/group bonding/%s_group_bonding.csv' % project, 'wb'))
#Attributes for Gephi
csv_attributes = csv.writer(open('results/networks/%s_at_node_attributes.csv' % project, 'wb'))
csv_writer.writerow(["Project", "Name", "Member_count", "Competing_Lists",
"FF_Nodes", "AT_Nodes", "RT_Nodes",
"FF_Edges","AT_Edges", "RT_Edges",
"FF_bin_density", "AT_density",
"FF_bin_avg_path_length", "AT_bin_avg_path_length",
"FF_bin_clustering", "AT_bin_clustering",
"FF_reciprocity", "AT_reciprocity",
"FF_bin_transitivity", "AT_bin_transitivity",
"RT_density", "RT_total_volume"
])
# Read in the networks
FF_all = nx.read_edgelist('data/networks/%s_FF.edgelist' % project, nodetype=str, data=(('weight',float),),create_using=nx.DiGraph())
AT_all = nx.read_edgelist('data/networks/%s_solr_AT.edgelist' % project, nodetype=str, data=(('weight',float),),create_using=nx.DiGraph())
RT_all = nx.read_edgelist('data/networks/%s_solr_RT.edgelist' % project, nodetype=str, data=(('weight',float),),create_using=nx.DiGraph())
# Read in the partition
tmp = hp.get_partition(partitionfile)
partitions = tmp[0]
groups = tmp[1]
#Read in members count for each project
reader = csv.reader(open("results/stats/%s_lists_stats.csv" % project, "rb"), delimiter=",")
temp = {}
reader.next() # Skip first row
for row in reader:
temp[row[0]] = {"name":row[0],"member_count":int(row[3])}
#Read in the list-listings for individuals
listings = {}
indiv_reader = csv.reader(open(partitionfile))
for row in indiv_reader:
if listings.has_key(row[1]):
listings[row[1]]["competing_lists"] += int(row[3])
else:
listings[row[1]] = {"competing_lists": int(row[3])}
i = 0
for partition in partitions:
for node in partition:
FF_all.add_node(node, group = groups[i])
AT_all.add_node(node, group = groups[i])
RT_all.add_node(node, group = groups[i])
i += 1
#Write out to pajek for gephi visualization
if to_pajek:
#Write the attributes file
i= 0
csv_attributes.writerow(["id", "name", "type"])
for node in AT_all.nodes():
i+= 1
csv_attributes.writerow([i, node, AT_all.node[node]["group"]])
nx.write_pajek(FF_all,"results/networks/%s_FF.net" % project)
nx.write_pajek(AT_all,"results/networks/%s_AT.net" % project)
nx.write_pajek(RT_all,"results/networks/%s_RT.net" % project)
i = 0
for partition in partitions:
project_name = groups[i]
# Add up total members
member_count = 0
member_count = int(temp[project_name]["member_count"])
print "############ Calculating Project %s ############### " % project_name
# Generate a subgraph according to the partition
FF = FF_all.subgraph(partition)
AT = AT_all.subgraph(partition)
RT = RT_all.subgraph(partition)
#Additional Info for each project
FF.name = "FF_%s " % project_name
AT.name = "AT_%s " % project_name
RT.name = "RT_%s " % project_name
############### Compute Group measures ################
#Measures FF
FF_bin_density = nx.density(FF)
FF_bin_transitivity = nx.transitivity(FF)
FF_reciprocity = hp.reciprocity(FF) # Calculate the number of reciprocated ties of all ties
# Measures that need a connected graph
# In case the graph is split into multiple graphs get the biggest connected component
FF_partition = nx.weakly_connected_components(FF)[0]
FF_comp = FF.subgraph(FF_partition)
FF_bin_avg_path_length = nx.average_shortest_path_length(FF_comp)
FF_bin_clustering = nx.average_clustering(FF_comp.to_undirected(),count_zeros=False) # Networks with a lot of mutual trust have a high clustering coefficient. # Star networks with a single broadcast node and passive listeners have a low clustering coefficient.
# Measures AT
#AT_density = nx.density(AT) # deprecated since it treats the network as binarized and we lose all the interaction information
AT_density = hp.average_tie_strength(AT)
AT_bin_transitivity = nx.transitivity(AT)
AT_reciprocity = hp.reciprocity(AT)
#AT_avg_volume = hp.average_tie_strength(AT)
AT_partition = nx.weakly_connected_components(AT)[0]
AT_comp = AT.subgraph(AT_partition)
AT_bin_avg_path_length = nx.average_shortest_path_length(AT_comp)
AT_bin_clustering = nx.average_clustering(AT_comp.to_undirected())
# Dependent Variable
#RT_density = nx.density(RT) # Danger this works on the binarized graph! # TODO I need a weighted density for RT
RT_density = hp.average_tie_strength(RT)
RT_total_volume = hp.total_edge_weight(RT)
############### Output ################
csv_writer.writerow([project, project_name, member_count, listings[project_name]["competing_lists"],
len(FF.nodes()), len(AT.nodes()), len(RT.nodes()),
len(FF.edges()), len(AT.edges()), len(RT.edges()),
FF_bin_density, AT_density,
FF_bin_avg_path_length, AT_bin_avg_path_length,
FF_bin_clustering, AT_bin_clustering,
FF_reciprocity, AT_reciprocity,
FF_bin_transitivity, AT_bin_transitivity,
RT_density, RT_total_volume])
i += 1
