Article Rank (Beta)

ArticleRank is an algorithm that has been derived from the PageRank algorithm to measure the influence of journal articles.

Page Rank assumes that relationships originating from low-degree nodes have a higher influence than relationships from high-degree nodes. Article Rank modifies the formula in such a way that it retains the basic PageRank methodology but lowers the influence of low-degree nodes.

The Article Rank of a node v at iteration i is defined as:

Within the formula:

• Nin(v) are the incoming neighbors and Nout(v) are the outgoing neighbors of node v.

• d is a damping factor in [0, 1], usually set to 0.85.

• Nout is the average out-degree

For more information, see ArticleRank: a PageRank‐based alternative to numbers of citations for analysing citation network.

Specifications

CREATE QUERY tg_article_rank (STRING v_type, STRING e_type,
 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 = "")

Parameters

Name	Description	Data type
v_type	A vertex type.	STRING
e_type	An edge type.	STRING
max_change	Article Rank 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.	FLOAT
max_iter	Maximum number of iterations.	INT
damping	The damping factor. Usually set to 0.85.	FLOAT
top_k	The number of results with the highest scores to return.	INT
print_accum	If true, print JSON output.	BOOL
result_attr	If true, store the article rank score of each vertex in this attribute.	STRING
file_path	If true, output CSV to this file.	STRING

Return value

The article rank score for each vertex.

Example

Suppose we have the following graph:

By running Article Rank on the graph, we will see that the vertex with the highest score is Dan:

Query

RUN QUERY tg_article_rank ("person", "friendship", _, _, _, _, _)

Result

{
  "error": false,
  "message": "",
  "version": {
    "schema": 2,
    "edition": "enterprise",
    "api": "v2"
  },
  "results": [{"@@topScores": [
    {
      "score": 2348294.75,
      "Vertex_ID": "Dan"
    },
    {
      "score": 1863160.625,
      "Vertex_ID": "Jenny"
    },
    {
      "score": 1442890.5,
      "Vertex_ID": "Tom"
    },
    {
      "score": 1053484.625,
      "Vertex_ID": "Nancy"
    },
    {
      "score": 739327.9375,
      "Vertex_ID": "Kevin"
    },
    {
      "score": 703562.75,
      "Vertex_ID": "Amily"
    },
    {
      "score": 498013.25,
      "Vertex_ID": "Jack"
    }
  ]}]
}