def test_katz_centrality_multi_column(graph_file):
gc.collect()
cu_M = utils.read_csv_file(graph_file)
cu_M.rename(columns={'0': 'src_0', '1': 'dst_0'}, inplace=True)
cu_M['src_1'] = cu_M['src_0'] + 1000
cu_M['dst_1'] = cu_M['dst_0'] + 1000
G1 = cugraph.DiGraph()
G1.from_cudf_edgelist(cu_M, source=["src_0", "src_1"],
destination=["dst_0", "dst_1"])
G2 = cugraph.DiGraph()
G2.from_cudf_edgelist(cu_M, source="src_0", destination="dst_0")
k_df_exp = cugraph.katz_centrality(G2, alpha=None, max_iter=1000)
k_df_exp = k_df_exp.sort_values("vertex").reset_index(drop=True)
nstart = cudf.DataFrame()
nstart['vertex_0'] = k_df_exp['vertex']
nstart['vertex_1'] = nstart['vertex_0'] + 1000
nstart['values'] = k_df_exp['katz_centrality']
k_df_res = cugraph.katz_centrality(G1, nstart=nstart,
alpha=None, max_iter=1000)
k_df_res = k_df_res.sort_values("0_vertex").reset_index(drop=True)
k_df_res.rename(columns={'0_vertex': 'vertex'}, inplace=True)
top_res = topKVertices(k_df_res, "katz_centrality", 10)
top_exp = topKVertices(k_df_exp, "katz_centrality", 10)
assert top_res.equals(top_exp)
def test_katz_centrality_nx(graph_file):
gc.collect()
NM = utils.read_csv_for_nx(graph_file)
Gnx = nx.from_pandas_edgelist(NM,
create_using=nx.DiGraph(),
source="0",
target="1")
G = cugraph.utilities.convert_from_nx(Gnx)
largest_out_degree = G.degrees().nlargest(n=1, columns="out_degree")
largest_out_degree = largest_out_degree["out_degree"].iloc[0]
katz_alpha = 1 / (largest_out_degree + 1)
nk = nx.katz_centrality(Gnx, alpha=katz_alpha)
ck = cugraph.katz_centrality(Gnx, alpha=None, max_iter=1000)
# Calculating mismatch
nk = sorted(nk.items(), key=lambda x: x[0])
ck = sorted(ck.items(), key=lambda x: x[0])
err = 0
assert len(ck) == len(nk)
for i in range(len(ck)):
if (abs(ck[i][1] - nk[i][1]) > 0.1 and ck[i][0] == nk[i][0]):
err = err + 1
print("Mismatches:", err)
assert err < (0.1 * len(ck))
def katz_centrality(G, as_dict=False):
W = timestep_cache().W
nodes_centrality = cugraph.katz_centrality(W)
nodes = np.array(list(nodes_centrality.keys()))
off_nodes = np.array(G.nodes)
clusters = np.array(list(nodes_centrality.values()))
s_clusters = ordered(off_nodes, nodes, clusters) # Need to sort the cluster cause the order is not keeped from louvain algo
if as_dict:
return dict(zip(off_nodes, s_clusters))
return s_clusters
def calc_katz(graph_file):
M = utils.read_csv_file(graph_file)
G = cugraph.Graph()
G.add_edge_list(M['0'], M['1'])
largest_out_degree = G.degrees().nlargest(n=1, columns='out_degree')
largest_out_degree = largest_out_degree['out_degree'][0]
katz_alpha = 1 / (largest_out_degree + 1)
k = cugraph.katz_centrality(G, katz_alpha, max_iter=1000)
NM = utils.read_csv_for_nx(graph_file)
NM = NM.tocsr()
Gnx = nx.DiGraph(NM)
nk = nx.katz_centrality(Gnx, alpha=katz_alpha)
pdf = pd.DataFrame(nk, index=[0]).T
k['nx_katz'] = pdf[0]
k = k.rename({'katz_centrality': 'cu_katz'})
return k
def calc_katz(graph_file):
cu_M = utils.read_csv_file(graph_file)
G = cugraph.DiGraph()
G.from_cudf_edgelist(cu_M, source='0', destination='1')
largest_out_degree = G.degrees().nlargest(n=1, columns='out_degree')
largest_out_degree = largest_out_degree['out_degree'].iloc[0]
katz_alpha = 1/(largest_out_degree + 1)
k_df = cugraph.katz_centrality(G, katz_alpha, max_iter=1000)
NM = utils.read_csv_for_nx(graph_file)
Gnx = nx.from_pandas_edgelist(NM, create_using=nx.DiGraph(),
source='0', target='1')
nk = nx.katz_centrality(Gnx, alpha=katz_alpha)
pdf = [nk[k] for k in sorted(nk.keys())]
k_df['nx_katz'] = pdf
k_df = k_df.rename({'katz_centrality': 'cu_katz'})
return k_df
def calc_katz(graph_file):
cu_M = utils.read_csv_file(graph_file)
G = cugraph.DiGraph()
G.from_cudf_edgelist(cu_M, source="0", destination="1")
largest_out_degree = G.degrees().nlargest(n=1, columns="out_degree")
largest_out_degree = largest_out_degree["out_degree"].iloc[0]
katz_alpha = 1 / (largest_out_degree + 1)
k_df = cugraph.katz_centrality(G, alpha=None, max_iter=1000)
k_df = k_df.sort_values("vertex").reset_index(drop=True)
NM = utils.read_csv_for_nx(graph_file)
Gnx = nx.from_pandas_edgelist(
NM, create_using=nx.DiGraph(), source="0", target="1"
)
nk = nx.katz_centrality(Gnx, alpha=katz_alpha)
pdf = [nk[k] for k in sorted(nk.keys())]
k_df["nx_katz"] = pdf
k_df = k_df.rename(columns={"katz_centrality": "cu_katz"}, copy=False)
return k_df
def katz(G, alpha=None):
return cugraph.katz_centrality(G, alpha)