Weighted PageRank

The only difference between weighted PageRank and standard PageRank is that edges have weights, and the influence that a vertex receives from an in-neighbor is multiplied by the weight of the in-edge.


tg_pageRank_wt (SET<STRING> v_type, SET<STRING> e_type, STRING wt_attr,
  FLOAT max_change=0.001, INT max_iter=25, FLOAT damping=0.85, INT top_k=100,
   BOOL print_accum = TRUE, STRING result_attr =  "", STRING file_path = "",
   BOOL display_edges = FALSE)
Characteristic Value


Computes a weighted PageRank value (FLOAT type) for each vertex.

Input Parameters

  • STRING v_type: Names of vertex type to use

  • STRING e_type: Names of edge type to use

  • STRING wt_attr: Name of edge weight attribute

  • FLOAT max_change: PageRank will stop iterating when the largest difference between any vertex’s current score and its previous score ≤ max_change. That is, the scores have become very stable and are changing by less than max_change from one iteration to the next.

  • INT max_iter: Maximum number of iterations.

  • FLOAT damping: Fraction of score that is due to the score of neighbors. The balance (1 - damping) is a minimum baseline score that every vertex receives.

  • INT top_k: Sort the scores highest first and output only this many scores

  • BOOL print_accum: If True, output JSON to standard output

  • STRING result_attr: If not empty, store PageRank values (FLOAT) to this attribute

  • STRING file_path: If not empty, write output to this file.

  • BOOL display_edges: If true, include the graph’s edges in the JSON output, so that the full graph can be displayed.

Result Size

V = number of vertices

Time Complexity

O(E*k), E = number of edges, k = number of iterations.

The number of iterations is data-dependent, but the user can set a maximum. Parallel processing reduces the time needed for computation.

Graph Types

Directed edges