diff options
Diffstat (limited to 'networkx/algorithms/link_analysis/pagerank_alg.py')
| -rw-r--r-- | networkx/algorithms/link_analysis/pagerank_alg.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/networkx/algorithms/link_analysis/pagerank_alg.py b/networkx/algorithms/link_analysis/pagerank_alg.py index b30b2437..ece444c8 100644 --- a/networkx/algorithms/link_analysis/pagerank_alg.py +++ b/networkx/algorithms/link_analysis/pagerank_alg.py @@ -134,21 +134,21 @@ def _pagerank_python( x = dict.fromkeys(W, 1.0 / N) else: # Normalized nstart vector - s = float(sum(nstart.values())) + s = sum(nstart.values()) x = {k: v / s for k, v in nstart.items()} if personalization is None: # Assign uniform personalization vector if not given p = dict.fromkeys(W, 1.0 / N) else: - s = float(sum(personalization.values())) + s = sum(personalization.values()) p = {k: v / s for k, v in personalization.items()} if dangling is None: # Use personalization vector if dangling vector not specified dangling_weights = p else: - s = float(sum(dangling.values())) + s = sum(dangling.values()) dangling_weights = {k: v / s for k, v in dangling.items()} dangling_nodes = [n for n in W if W.out_degree(n, weight=weight) == 0.0] @@ -359,7 +359,7 @@ def pagerank_numpy(G, alpha=0.85, personalization=None, weight="weight", danglin ind = np.argmax(eigenvalues) # eigenvector of largest eigenvalue is at ind, normalized largest = np.array(eigenvectors[:, ind]).flatten().real - norm = float(largest.sum()) + norm = largest.sum() return dict(zip(G, map(float, largest / norm))) |