def test_k_truss(self):
# k=-1
k_truss_subgraph = nx.k_truss(self.G, -1)
assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21))
# k=0
k_truss_subgraph = nx.k_truss(self.G, 0)
assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21))
# k=1
k_truss_subgraph = nx.k_truss(self.G, 1)
assert sorted(k_truss_subgraph.nodes()) == list(range(1, 13))
# k=2
k_truss_subgraph = nx.k_truss(self.G, 2)
assert sorted(k_truss_subgraph.nodes()) == list(range(1, 9))
# k=3
k_truss_subgraph = nx.k_truss(self.G, 3)
assert sorted(k_truss_subgraph.nodes()) == []
def test_ktruss_subgraph_Graph_nx(graph_file, nx_ground_truth):
gc.collect()
k = 5
M = utils.read_csv_for_nx(graph_file, read_weights_in_sp=True)
G = nx.from_pandas_edgelist(M,
source="0",
target="1",
edge_attr="weight",
create_using=nx.Graph())
k_subgraph = cugraph.k_truss(G, k)
k_truss_nx = nx.k_truss(G, k)
assert nx.is_isomorphic(k_subgraph, k_truss_nx)
def max_ktruss(G, min_truss, max_truss):
"""Computes, plots and returns max k-truss value
Searches for a maximum k-truss between min_truss and max_truss
max_truss can be set arbitrarily large
"""
verbose = 0
G_save = G
itruss = min_truss
# look for max k-truss
for truss in range(min_truss, max_truss + 1):
G_truss = nx.k_truss(G, truss)
if G_truss.number_of_nodes() == 0: # Max k-truss reached
print(f'Max-truss = {itruss}')
break
else:
G_save = G_truss
itruss += 1
if verbose: print(f"Nodes in k-truss : {G_save.nodes()}")
return itruss, G_save
def decomposition_democrat(GU, core=False, truss=False):
# Democrat Graph
nodes = (
node
for node, data in GU.nodes(data=True)
if data.get("Party") == "Democrat"
)
largest_cc = max(nx.connected_components(nx.Graph(GU.subgraph(nodes))), key=len)
GU_Democrat = GU.subgraph(largest_cc)
if core:
nodes = nx.k_core(GU_Democrat, k=12).nodes()
elif truss:
nodes = nx.k_truss(GU_Democrat, k=4).nodes()
node_color = []
for node in GU_Democrat.nodes():
if node in nodes:
node_color.append("yellow")
else:
node_color.append("blue")
d = nx.degree(GU_Democrat)
sizes = [(d[node] + 1) * 5 for node in GU_Democrat.nodes()]
# Specify the settings for the Force Atlas 2 algorithm
forceatlas2 = ForceAtlas2(
# Behavior alternatives
outboundAttractionDistribution=True, # Dissuade hubs
linLogMode=False, # NOT IMPLEMENTED
adjustSizes=False, # Prevent overlap (NOT IMPLEMENTED)
edgeWeightInfluence=1.0,
# Performance
jitterTolerance=1.0, # Tolerance
barnesHutOptimize=True,
barnesHutTheta=1.2,
multiThreaded=False, # NOT IMPLEMENTED
# Tuning
scalingRatio=2.0,
strongGravityMode=False,
gravity=5.0,
# Log
verbose=True)
positions = forceatlas2.forceatlas2_networkx_layout(GU, pos=None, iterations=2000)
plt.figure(num=None, figsize=(15, 9), dpi=80, facecolor='w', edgecolor='k')
sns.set_style('whitegrid')
sns.set_context('talk')
# create legend
plt.scatter([], [], c='yellow', alpha=0.7, s=100, label='Most Influential Democrats')
plt.legend(scatterpoints=1, frameon=True, labelspacing=1)
nx.draw_networkx_nodes(GU_Democrat, positions, node_size=sizes, node_color=node_color, alpha=0.7)
nx.draw_networkx_edges(GU_Democrat, positions, edge_color="grey", alpha=0.08)
ax = plt.gca()
ax.collections[0].set_linewidth(0.1)
ax.set_title('US House Democrat Representatives of 2020 network', fontsize=16);
plt.axis('off')
if core:
plt.savefig("./images/core_democrat.png", format="PNG")
elif truss:
plt.savefig("./images/truss_democrat.png", format="PNG")
return nodes, GU_Democrat
import networkx as nx
import configuration as conf
import utils as ut
from itertools import combinations
if __name__ == "__main__":
dataset = ut.load_obj(conf.data_dir, conf.dataset)
G = nx.Graph()
for v in dataset:
if len(v) > 1:
G.add_edges_from([t for t in combinations(v, 2)])
ut.save_obj(G, conf.data_dir, f'{conf.dataset}_graph')
trussness_g = {}
k = 1
while k < 100:
ktruss = nx.k_truss(G, k)
if len(ktruss) > 0:
trussness_g[k] = ktruss
k += 1
else:
break
ut.save_obj(trussness_g, conf.data_dir, f'{conf.dataset}_graph_trussness')
