Influence maximization is the problem of finding a small subset of vertices in a social network that could maximize the spread of influence.
There are two versions of the Influence Maximization algorithm. Both versions find k
vertices that maximize the expected spread of influence in the network. The CELF version improves upon the efficiency of the greedy version and should be preferred in analyzing large networks.
The two versions of the algorithm are implemented on the following papers:
The CELF version and the greedy version of the algorithms share the same set of parameters.
The ID of the vertices with the highest influence scores along with their scores.
Name
Description
Data type
v_type
A vertex type
STRING
e_type
An edge type
STRING
weight
The name of the weight attribute on the edge type
STRING
top_k
The number of vertices with the highest influence score to return
INT
print_accum
If true, print results to JSON output.
BOOL
file_path
If not empty, save results in CSV to this file.
STRING