def networkx_to_katanagraph(x: NetworkXGraph, **props) -> KatanaGraph:
aprops = NetworkXGraph.Type.compute_abstract_properties(
x, {"node_dtype", "node_type", "edge_type", "is_directed"})
is_weighted = aprops["edge_type"] == "map"
# get the edge list directly from the NetworkX Graph
elist_raw = list(x.value.edges(data=True))
# sort the eddge list and node list
if aprops["is_directed"]:
elist = sorted(elist_raw, key=lambda each: (each[0], each[1]))
else:
inv_elist = [(each[1], each[0], each[2]) for each in elist_raw]
elist = sorted(elist_raw + inv_elist,
key=lambda each: (each[0], each[1]))
nlist = sorted(list(x.value.nodes(data=True)), key=lambda each: each[0])
# build the CSR format from the edge list (weight, (src, dst))
row = np.array([each_edge[0] for each_edge in elist])
col = np.array([each_edge[1] for each_edge in elist])
data = np.array([each_edge[2]["weight"] for each_edge in elist])
csr = csr_matrix((data, (row, col)), shape=(len(nlist), len(nlist)))
# call the katana api to build a Graph (unweighted) from the CSR format
# noting that the first 0 in csr.indptr is excluded
pg = from_csr(csr.indptr[1:], csr.indices)
# add the edge weight as a new property
t = pyarrow.table(dict(value_from_translator=data))
pg.add_edge_property(t)
# use the metagraph's Graph warpper to wrap the katana.local.Graph
return KatanaGraph(
pg_graph=pg,
is_weighted=is_weighted,
edge_weight_prop_name="value_from_translator",
is_directed=aprops["is_directed"],
node_weight_index=0,
)
def test_from_csr_k3():
pg = from_csr(np.array([2, 4, 6]), np.array([1, 2, 0, 2, 0, 1]))
assert pg.num_nodes() == 3
assert pg.num_edges() == 6
assert list(pg.edge_ids(2)) == [4, 5]
assert pg.get_edge_dest(4) == 0
assert pg.get_edge_dest(5) == 1
def test_from_csr_int16():
pg = from_csr(np.array([1, 1], dtype=np.int16),
np.array([1], dtype=np.int16))
assert pg.num_nodes() == 2
assert pg.num_edges() == 1
# assert list(pg.out_edge_ids(0)) == [0]
assert pg.get_edge_dst(0) == 1
def gen_pg_cleaned_8_12_from_csr(is_directed):
"""
A helper function for the test, generating Katana's Graph from an edge list
"""
katana.local.initialize()
elist_raw = [
(0, 1, 4),
(0, 3, 2),
(0, 4, 7),
(1, 3, 3),
(1, 4, 5),
(2, 4, 5),
(2, 5, 2),
(2, 6, 8),
(3, 4, 1),
(4, 7, 4),
(5, 6, 4),
(5, 7, 6),
]
src_list = [each[0] for each in elist_raw]
dest_list = [each[1] for each in elist_raw]
nlist_raw = list(set(src_list) | set(dest_list))
# sort the eddge list and node list
if is_directed:
elist = sorted(elist_raw, key=lambda each: (each[0], each[1]))
else:
inv_elist = [(each[1], each[0], each[2]) for each in elist_raw]
elist = sorted(elist_raw + inv_elist,
key=lambda each: (each[0], each[1]))
nlist = sorted(nlist_raw, key=lambda each: each)
# build the CSR format from the edge list (weight, (src, dst))
row = np.array([each_edge[0] for each_edge in elist])
col = np.array([each_edge[1] for each_edge in elist])
data = np.array([each_edge[2] for each_edge in elist])
csr = csr_matrix((data, (row, col)), shape=(len(nlist), len(nlist)))
# call the katana api to build a Graph (unweighted) from the CSR format
# noting that the first 0 in csr.indptr is excluded
pg = from_csr(csr.indptr[1:], csr.indices)
t = pyarrow.table(dict(value=data))
pg.add_edge_property(t)
return pg
def test_from_csr():
pg = from_csr(np.array([1, 1], dtype=np.uint32), np.array([1], dtype=np.uint64))
assert pg.num_nodes() == 2
assert pg.num_edges() == 1
assert list(pg.edge_ids(0)) == [0]
assert pg.get_edge_dest(0) == 1
