Skip to content

Fix computing first-travel weights #32

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Feb 8, 2020
Merged

Fix computing first-travel weights #32

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 9 additions & 9 deletions node2vec/node2vec.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,6 @@ def _precompute_probabilities(self):
"""

d_graph = self.d_graph
first_travel_done = set()

nodes_generator = self.graph.nodes() if self.quiet \
else tqdm(self.graph.nodes(), desc='Computing transition probabilities')
Expand All @@ -90,7 +89,6 @@ def _precompute_probabilities(self):
d_graph[current_node][self.PROBABILITIES_KEY] = dict()

unnormalized_weights = list()
first_travel_weights = list()
d_neighbors = list()

# Calculate unnormalized weights
Expand All @@ -110,23 +108,25 @@ def _precompute_probabilities(self):

# Assign the unnormalized sampling strategy weight, normalize during random walk
unnormalized_weights.append(ss_weight)
if current_node not in first_travel_done:
first_travel_weights.append(self.graph[current_node][destination].get(self.weight_key, 1))
d_neighbors.append(destination)

# Normalize
unnormalized_weights = np.array(unnormalized_weights)
d_graph[current_node][self.PROBABILITIES_KEY][
source] = unnormalized_weights / unnormalized_weights.sum()

if current_node not in first_travel_done:
unnormalized_weights = np.array(first_travel_weights)
d_graph[current_node][self.FIRST_TRAVEL_KEY] = unnormalized_weights / unnormalized_weights.sum()
first_travel_done.add(current_node)

# Save neighbors
d_graph[current_node][self.NEIGHBORS_KEY] = d_neighbors

# Calculate first_travel weights for source
first_travel_weights = []

for destination in self.graph.neighbors(source):
first_travel_weights.append(self.graph[source][destination].get(self.weight_key, 1))

first_travel_weights = np.array(first_travel_weights)
d_graph[source][self.FIRST_TRAVEL_KEY] = first_travel_weights / first_travel_weights.sum()

def _generate_walks(self) -> list:
"""
Generates the random walks which will be used as the skip-gram input.
Expand Down