def count_in_and_out_degree(graph: Graph, nout, nin, nid):
out_degree = 0
for edge in graph.out_edge_ids_for_node(nid):
out_degree += 1
dst = graph.out_edge_dst(edge)
atomic_add(nin, dst, 1)
nout[nid] = out_degree
def cc_pull_topo_operator(graph: Graph, changed, comp_current: np.ndarray, nid):
for ii in graph.out_edge_ids_for_node(nid):
dst = graph.out_edge_dst(ii)
# Pull the minimum component from your neighbors
if comp_current[nid] > comp_current[dst]:
comp_current[nid] = comp_current[dst]
# Indicates that update happened
changed.update(True)
def compute_async_kcore_operator(graph: Graph, current_degree, k_core_num, nid, ctx):
# Decrement degree of all the neighbors of dead node
for ii in graph.out_edge_ids_for_node(nid):
dst = graph.out_edge_dst(ii)
old_degree = atomic_sub(current_degree, dst, 1)
# Add new dead nodes to the worklist
if old_degree == k_core_num:
ctx.push(dst)
def count_weighted_in_and_out_degree(graph: Graph, nout, nin, weight_array, nid):
out_degree = 0
for edge in graph.out_edge_ids(nid):
weight = weight_array[edge]
out_degree += weight
dst = graph.out_edge_dst(edge)
atomic_add(nin, dst, weight)
nout[nid] = out_degree
def compute_pagerank_pull_residual_operator(graph: Graph, delta, residual, nid):
total = 0
for ii in graph.out_edge_ids_for_node(nid):
dst = graph.out_edge_dst(ii)
if delta[dst] > 0:
total += delta[dst]
if total > 0:
residual[nid] = total
def cc_push_topo_operator(graph: Graph, changed, comp_current: np.ndarray, comp_old: np.ndarray, nid):
if comp_old[nid] > comp_current[nid]:
comp_old[nid] = comp_current[nid]
# Indicates that update happened
changed.update(True)
for ii in graph.out_edge_ids_for_node(nid):
dst = graph.out_edge_dst(ii)
new_comp = comp_current[nid]
# Push the minimum component to your neighbors
atomic_min(comp_current, dst, new_comp)
def sssp_operator(g: Graph, dists: np.ndarray, edge_weights, item,
ctx: UserContext):
if dists[item.src] < item.dist:
return
for ii in g.out_edge_ids_for_node(item.src):
dst = g.out_edge_dst(ii)
edge_length = edge_weights[ii]
new_distance = edge_length + dists[item.src]
old_distance = atomic_min(dists, dst, new_distance)
if new_distance < old_distance:
ctx.push((dst, new_distance))
def bfs_sync_operator_pg(
graph: Graph,
next_level: InsertBag[np.uint64],
next_level_number: int,
distance: np.ndarray,
nid,
):
for ii in graph.out_edge_ids(nid):
dst = graph.out_edge_dst(ii)
if distance[dst] == distance_infinity:
distance[dst] = next_level_number
next_level.push(dst)
def sum_degree_operator(
graph: Graph,
source_degree,
sum_source: ReduceSum[np.uint64],
destination_degree,
sum_destination: ReduceSum[np.uint64],
nid,
):
for edge in graph.out_edge_ids_for_node(nid):
sum_source.update(source_degree[nid])
dst = graph.out_edge_dst(edge)
sum_destination.update(destination_degree[dst])
def degree_assortativity_coefficient_operator(
graph: Graph,
source_degree,
source_average,
destination_degree,
destination_average,
product_of_dev: ReduceSum[float],
square_of_source_dev: ReduceSum[float],
square_of_destination_dev: ReduceSum[float],
nid,
):
# deviation of source node from average
source_dev = source_degree[nid] - source_average
for edge in graph.out_edge_ids_for_node(nid):
dst = graph.out_edge_dst(edge)
destination_dev = destination_degree[dst] - destination_average
product_of_dev.update(source_dev * destination_dev)
square_of_source_dev.update(source_dev * source_dev)
square_of_destination_dev.update(destination_dev * destination_dev)
def compute_out_deg_operator(graph: Graph, nout, nid):
"""Operator for computing outdegree of nodes in the Graph"""
for ii in graph.out_edge_ids_for_node(nid):
dst = graph.out_edge_dst(ii)
atomic_add(nout, dst, 1)
