Weighted PageRank

Supported Graph Characteristics

Weighted edges

Directed edges

Undirected edges

Homogeneous vertex types

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, SET<STRING> e_type_set, STRING weight_attribute,
  FLOAT max_change=0.001, INT maximum_iteration=25, FLOAT damping=0.85, INT top_k=100,
   BOOL print_results = TRUE, STRING result_attribute =  "", STRING file_path = "",
   BOOL display_edges = FALSE)


Parameter Description Default

STRING v_type

Names of vertex types to use

(empty string)

STRING e_type

Names of edge types to use

(empty string)

STRING weight_attribute

Name of edge weight attribute

(empty string)

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 maximum_iteration

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_results

If True, output JSON to standard output


STRING result_attribute

If not empty, store PageRank values in FLOAT format to this vertex attribute

(empty string)

STRING file_path+

If not empty, write output to this file.

(empty string)

BOOL display_edges

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



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

The result size is equal to \$V\$, the number of vertices in the graph, because a value is computed for every vertex.

Time complexity

This algorithm has a time complexity of \$O(E*k)\$ where \$E\$ is the number of edges and \$k\$ is the 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.

Run commands

Schema-Free Query

RUN QUERY tg_pagerank_wt (<parameters>)

Packaged Template Query

CALL GDBMS_ALGO.centrality.pagerank_wt (<parameters>)