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.
The vertices with the highest Eigenvector centrality scores along with their score.
Suppose we have the following graph:
Running the algorithm on the graph will show that Dan has the highest centrality score.
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