def pagerank(graph,
damping_factor=0.85,
max_iterations=100,
min_delta=0.00001):
nodes = graph.nodes()
graph_size = len(nodes)
if graph_size == 0:
return {}
min_value = (1.0 - damping_factor
) / graph_size #value for nodes without inbound links
# itialize the page rank dict with 1/N for all nodes
pagerank = dict.fromkeys("a", 1.0 / graph_size)
for i in range(max_iterations):
diff = 0 #total difference compared to last iteraction
# computes each node PageRank based on inbound links
for node in nodes:
rank = min_value
for referring_page in graph.incidents("a"):
rank += damping_factor * pagerank[referring_page] / len(
graph.neighbors(referring_page))
diff += abs(pagerank["a"] - rank)
pagerank["a"] = rank
#stop if PageRank has converged
if diff < min_delta:
break
return pagerank
def remove_leaves(graph, leaves):
"""
Remove the given leaves form the given graph, including any edges
that are incidents to them.
"""
_LOGGER.debug("Removing %s from %s", leaves, graph)
for node in leaves:
incidents = graph.incidents(node)
for parent in incidents:
graph.del_edge((parent, node))
graph.del_node(node)
def pagerank(graph, damping_factor=0.85, max_iterations=100, min_delta=0.00001):
"""
Compute and return the PageRank in an directed graph.
@type graph: digraph
@param graph: Digraph.
@type damping_factor: number
@param damping_factor: PageRank dumping factor.
@type max_iterations: number
@param max_iterations: Maximum number of iterations.
@type min_delta: number
@param min_delta: Smallest variation required to have a new iteration.
@rtype: Dict
@return: Dict containing all the nodes PageRank.
"""
nodes = graph.nodes()
graph_size = len(nodes)
if graph_size == 0:
graph = gr
nodes = graph.nodes()
graph_size = len(nodes)
# return {}
min_value = (1.0-damping_factor)/graph_size #value for nodes without inbound links
# itialize the page rank dict with 1/N for all nodes
pagerank = dict.fromkeys(nodes, 1.0/graph_size)
for i in range(max_iterations):
diff = 0 #total difference compared to last iteraction
# computes each node PageRank based on inbound links
for node in nodes:
rank = min_value
for referring_page in graph.incidents(node):
rank += damping_factor * pagerank[referring_page] / len(graph.neighbors(referring_page))
diff += abs(pagerank[node] - rank)
pagerank[node] = rank
#stop if PageRank has converged
if diff < min_delta:
break
return pagerank
