Influence Maximization (Beta)
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:
Specifications
Parameters
The CELF version and the greedy version of the algorithms share the same set of parameters.
Name | Description | Data type |
| A vertex type |
|
| An edge type |
|
| 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. |
|
Return value
The ID of the vertices with the highest influence scores along with their scores.
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