Influence Maximization
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.
Specifications
CREATE QUERY tg_influence_maximization_CELF(STRING v_type,STRING e_type, STRING weight_attribute, INT top_k, BOOL print_results = True, STRING file_path = "")
CREATE QUERY tg_influence_maximization_greedy(STRING v_type,
STRING e_type,STRING weight_attribute,INT top_k,
BOOL print_results = True, STRING file_path = "")
Parameters
The CELF version and the greedy version of the algorithms share the same set of parameters.
Name | Description |
---|---|
|
The vertex type to measure influence of |
|
The edge type to traverse |
|
The name of the weight attribute on the edge type |
|
The number of vertices with the highest influence score to return |
|
If true, print results to JSON output. |
|
If not empty, save results in CSV to this file. |