Eigenvector Centrality (Beta)

Eigenvector centrality (also called eigencentrality or prestige score) is a measure of the influence of a vertex in a network. Relative scores are assigned to all vertices in the network based on the concept that connections to high-scoring vertices contribute more to the score of the vertex in question than equal connections to low-scoring vertices. A high eigenvector score means that a vertex is connected to many vertices who themselves have high scores.

For more information, see Eigenvector centrality.

Specification

CREATE QUERY tg_eigenvector_cent(SET<STRING> v_type, SET<STRING> e_type, 
 INT maxIter = 100, FLOAT convLimit = 0.000001, INT top_k = 100, 
 BOOL print_accum = True, STRING result_attr = "", STRING file_path = ""
 )

Parameters

Name	Description	Data type
v_type	Vertex types to assign scores to.	SET<STRING>
e_type	Edge types to traverse.	SET<STRING>
maxIter	Maximum number of iteration.	INT
convLimit	The convergence limit.	FLOAT
top_k	The number of vertices with the highest scores to return.	INT
print_accum	If true, print results to JSON output.	BOOL
result_attr	If not empty, save the score of each vertex to this attribute.	STRING
file_path	If not empty, print results in CSV to this file.	STRING

Return value

The vertices with the highest Eigenvector centrality scores along with their score.

Example

Suppose we have the following graph:

Running the algorithm on the graph will show that Dan has the highest centrality score.

Query

RUN QUERY tg_eigenvector_cent(["person"], ["friendship"])

Result

{
  "error": false,
  "message": "",
  "version": {
    "schema": 2,
    "edition": "enterprise",
    "api": "v2"
  },
  "results": [{"top_scores": [
    {
      "score": 0.59598,
      "Vertex_ID": "Dan"
    },
    {
      "score": 0.50223,
      "Vertex_ID": "Jenny"
    },
    {
      "score": 0.44381,
      "Vertex_ID": "Tom"
    },
    {
      "score": 0.28786,
      "Vertex_ID": "Nancy"
    },
    {
      "score": 0.24085,
      "Vertex_ID": "Kevin"
    },
    {
      "score": 0.20296,
      "Vertex_ID": "Amily"
    },
    {
      "score": 0.11633,
      "Vertex_ID": "Jack"
    }
  ]}]
}