def test_k_core_corenumber_multicolumn(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.Graph()
G1.from_cudf_edgelist(cu_M,
source=["src_0", "src_1"],
destination=["dst_0", "dst_1"])
corenumber_G1 = cugraph.core_number(G1)
corenumber_G1.rename(columns={'core_number': 'values'}, inplace=True)
corenumber_G1 = corenumber_G1[['0_vertex', '1_vertex', 'values']]
ck_res = cugraph.k_core(G1, core_number=corenumber_G1)
G2 = cugraph.Graph()
G2.from_cudf_edgelist(cu_M, source="src_0", destination="dst_0")
ck_exp = cugraph.k_core(G2)
# FIXME: Replace with multi-column view_edge_list()
edgelist_df = ck_res.edgelist.edgelist_df
edgelist_df_res = ck_res.unrenumber(edgelist_df, "src")
edgelist_df_res = ck_res.unrenumber(edgelist_df_res, "dst")
for i in range(len(edgelist_df_res)):
assert ck_exp.has_edge(edgelist_df_res["0_src"].iloc[i],
edgelist_df_res["0_dst"].iloc[i])
def test_core_number_Graph_nx(graph_file):
gc.collect()
NM = utils.read_csv_for_nx(graph_file)
Gnx = nx.from_pandas_edgelist(NM,
source="0",
target="1",
create_using=nx.Graph())
nc = nx.k_core(Gnx)
cc = cugraph.k_core(Gnx)
assert nx.is_isomorphic(nc, cc)
def calc_k_cores(graph_file):
cu_M = utils.read_csv_file(graph_file)
G = cugraph.DiGraph()
G.from_cudf_edgelist(cu_M, source='0', destination='1')
ck = cugraph.k_core(G)
NM = utils.read_csv_for_nx(graph_file)
Gnx = nx.from_pandas_edgelist(NM,
source='0',
target='1',
create_using=nx.DiGraph())
nk = nx.k_core(Gnx)
return ck, nk
def calc_k_cores(graph_file):
cu_M = utils.read_csv_file(graph_file)
G = cugraph.DiGraph()
G.from_cudf_edgelist(cu_M, source='0', target='1')
ck = cugraph.k_core(G)
NM = utils.read_csv_for_nx(graph_file)
NM = NM.tocsr()
Gnx = nx.DiGraph(NM)
nk = nx.k_core(Gnx)
return ck, nk
def calc_k_cores(graph_file):
M = utils.read_csv_file(graph_file)
G = cugraph.Graph()
G.add_edge_list(M['0'], M['1'])
ck = cugraph.k_core(G)
NM = utils.read_csv_for_nx(graph_file)
NM = NM.tocsr()
Gnx = nx.DiGraph(NM)
nk = nx.k_core(Gnx)
return ck, nk
def calc_k_cores(graph_file, directed=True):
# directed is used to create either a Graph or DiGraph so the returned
# cugraph can be compared to nx graph of same type.
cu_M = utils.read_csv_file(graph_file)
NM = utils.read_csv_for_nx(graph_file)
if directed:
G = cugraph.DiGraph()
Gnx = nx.from_pandas_edgelist(NM, source='0', target='1',
create_using=nx.DiGraph())
else:
G = cugraph.Graph()
Gnx = nx.from_pandas_edgelist(NM, source='0', target='1',
create_using=nx.Graph())
G.from_cudf_edgelist(cu_M, source='0', destination='1')
ck = cugraph.k_core(G)
nk = nx.k_core(Gnx)
return ck, nk