ArticleRank
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 lowdegree vertices have a higher influence than relationships from highdegree vertices. Article Rank keeps the PageRank methodology but modifies the formula to lower the influence of lowdegree nodes.
The Article Rank of a node \(v\) at iteration \(i\) is defined as:
Within the formula:

\(N_{in}(v)\) are the incoming neighbors and \(N_{out}(v)\) are the outgoing neighbors of node \(v\).

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

\(\overline{N_{out}}\) is the average outdegree.
Notes
For more information, see ArticleRank: a PageRank‐based alternative to numbers of citations for analysing citation networks.
Specifications
CREATE QUERY tg_article_rank (STRING v_type, STRING e_type,
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 = "")
Parameters
Name  Description 


A vertex type. 

An edge type. 

Article Rank will stop iterating when the largest
difference between any vertex’s current score and its previous score ≤


Maximum number of iterations. 

The damping factor. Usually set to 0.85. 

The number of results with the highest scores to return. 

If true, print JSON output. 

If not empty, store the article rank score of each vertex in this attribute. 

If not empty, write output to this file. 
Result size
The result size is equal to \(V\), the number of vertices, because one score is produced for every vertex in the graph.
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:
RUN QUERY tg_article_rank ("person", "friendship", _, _, _, _, _)
{
"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"
}
]}]
}