def _transform_vertex_pair_valid(self,
graph,
vertex_v,
vertex_u,
radius,
distance,
feature_list,
connection_weight=1):
cw = connection_weight
# we need to revert to r/2 and d/2
radius_dist_key = (radius / 2, distance / 2)
# reweight using external weight dictionary
len_v = len(graph.nodes[vertex_v]['neigh_graph_hash'])
len_u = len(graph.nodes[vertex_u]['neigh_graph_hash'])
if radius < len_v and radius < len_u:
# feature as a pair of neighborhoods at a radius,distance
# canonicalization of pair of neighborhoods
vertex_v_labels = graph.nodes[vertex_v]['neigh_graph_hash']
vertex_v_hash = vertex_v_labels[radius]
vertex_u_labels = graph.nodes[vertex_u]['neigh_graph_hash']
vertex_u_hash = vertex_u_labels[radius]
if vertex_v_hash < vertex_u_hash:
first_hash, second_hash = (vertex_v_hash, vertex_u_hash)
else:
first_hash, second_hash = (vertex_u_hash, vertex_v_hash)
feature = fast_hash_4(first_hash, second_hash, radius, distance,
self.bitmask)
# half features are those that ignore the central vertex v
# the reason to have those is to help model the context
# independently from the identity of the vertex itself
half_feature = fast_hash_3(vertex_u_hash, radius, distance,
self.bitmask)
if graph.graph.get('weighted', False) is False:
if self.use_only_context is False:
feature_list[radius_dist_key][feature] += cw
feature_list[radius_dist_key][half_feature] += cw
else:
weight_v = graph.nodes[vertex_v]['neigh_graph_weight']
weight_u = graph.nodes[vertex_u]['neigh_graph_weight']
weight_vu_radius = weight_v[radius] + weight_u[radius]
val = cw * weight_vu_radius
# Note: add a feature only if the value is not 0
if val != 0:
if self.use_only_context is False:
feature_list[radius_dist_key][feature] += val
half_val = cw * weight_u[radius]
feature_list[radius_dist_key][half_feature] += half_val
def _transform_vertex_pair_valid(self,
graph,
vertex_v,
vertex_u,
radius,
distance,
feature_list,
connection_weight=1):
cw = connection_weight
# we need to revert to r/2 and d/2
radius_dist_key = (radius / 2, distance / 2)
# reweight using external weight dictionary
len_v = len(graph.nodes[vertex_v]['neigh_graph_hash'])
len_u = len(graph.nodes[vertex_u]['neigh_graph_hash'])
if radius < len_v and radius < len_u:
# feature as a pair of neighborhoods at a radius,distance
# canonicalization of pair of neighborhoods
vertex_v_labels = graph.nodes[vertex_v]['neigh_graph_hash']
vertex_v_hash = vertex_v_labels[radius]
vertex_u_labels = graph.nodes[vertex_u]['neigh_graph_hash']
vertex_u_hash = vertex_u_labels[radius]
if vertex_v_hash < vertex_u_hash:
first_hash, second_hash = (vertex_v_hash, vertex_u_hash)
else:
first_hash, second_hash = (vertex_u_hash, vertex_v_hash)
feature = fast_hash_4(
first_hash, second_hash, radius, distance, self.bitmask)
# half features are those that ignore the central vertex v
# the reason to have those is to help model the context
# independently from the identity of the vertex itself
half_feature = fast_hash_3(vertex_u_hash,
radius, distance, self.bitmask)
if graph.graph.get('weighted', False) is False:
feature_list[radius_dist_key][feature] += cw
feature_list[radius_dist_key][half_feature] += cw
else:
weight_v = graph.nodes[vertex_v]['neigh_graph_weight']
weight_u = graph.nodes[vertex_u]['neigh_graph_weight']
weight_vu_radius = weight_v[radius] + weight_u[radius]
val = cw * weight_vu_radius
# Note: add a feature only if the value is not 0
if val != 0:
feature_list[radius_dist_key][feature] += val
half_val = cw * weight_u[radius]
feature_list[radius_dist_key][half_feature] += half_val
def _transform_vertex_pair(self,
graph,
vertex_v,
vertex_u,
distance,
feature_list,
connection_weight=1):
# for all radii
for radius in range(self.min_r, self.r + 2, 2):
for label_index in range(graph.graph['label_size']):
if radius < len(graph.node[vertex_v]
['neigh_graph_hash'][label_index]) and \
radius < len(graph.node[vertex_u]['neigh_graph_hash'][label_index]):
# feature as a pair of neighborhoods at a radius,distance
# canonicalization of pair of neighborhoods
vertex_v_hash = graph.node[vertex_v]['neigh_graph_hash'][
label_index][radius]
vertex_u_hash = graph.node[vertex_u]['neigh_graph_hash'][
label_index][radius]
if vertex_v_hash < vertex_u_hash:
first_hash, second_hash = (vertex_v_hash,
vertex_u_hash)
else:
first_hash, second_hash = (vertex_u_hash,
vertex_v_hash)
feature = fast_hash_4(first_hash, second_hash, radius,
distance, self.bitmask)
# half features are those that ignore the central vertex v
# the reason to have those is to help model the context
# independently from the identity of the vertex itself
half_feature = fast_hash_3(vertex_u_hash, radius, distance,
self.bitmask)
key = fast_hash_2(radius, distance)
if graph.graph.get('weighted', False) is False:
feature_list[key][feature] += 1
feature_list[key][half_feature] += 1
else:
val = connection_weight * \
(graph.node[vertex_v]['neigh_graph_weight'][radius] +
graph.node[vertex_u]['neigh_graph_weight'][radius])
feature_list[key][feature] += val
half_val = \
connection_weight * \
graph.node[vertex_u]['neigh_graph_weight'][radius]
feature_list[key][half_feature] += half_val
def _transform_vertex_pair(self,
graph,
vertex_v,
vertex_u,
distance,
feature_list,
connection_weight=1):
# for all radii
for radius in range(self.min_r, self.r + 2, 2):
for label_index in range(graph.graph['label_size']):
if radius < len(graph.node[vertex_v]
['neigh_graph_hash'][label_index]) and \
radius < len(graph.node[vertex_u]['neigh_graph_hash'][label_index]):
# feature as a pair of neighborhoods at a radius,distance
# canonicalization of pair of neighborhoods
vertex_v_hash = graph.node[vertex_v]['neigh_graph_hash'][label_index][radius]
vertex_u_hash = graph.node[vertex_u]['neigh_graph_hash'][label_index][radius]
if vertex_v_hash < vertex_u_hash:
first_hash, second_hash = (vertex_v_hash,
vertex_u_hash)
else:
first_hash, second_hash = (vertex_u_hash,
vertex_v_hash)
feature = fast_hash_4(first_hash, second_hash,
radius, distance, self.bitmask)
# half features are those that ignore the central vertex v
# the reason to have those is to help model the context
# independently from the identity of the vertex itself
half_feature = fast_hash_3(vertex_u_hash,
radius, distance, self.bitmask)
key = fast_hash_2(radius, distance)
if graph.graph.get('weighted', False) is False:
feature_list[key][feature] += 1
feature_list[key][half_feature] += 1
else:
val = connection_weight * \
(graph.node[vertex_v]['neigh_graph_weight'][radius] +
graph.node[vertex_u]['neigh_graph_weight'][radius])
feature_list[key][feature] += val
half_val = \
connection_weight * \
graph.node[vertex_u]['neigh_graph_weight'][radius]
feature_list[key][half_feature] += half_val
