The repository contains the codes and data for the "Precise influence evaluation in complex networks".

For code, we provide the codes of our method ALGE and other algorithms for comparison of influence evaluation, which are stored in influence_evaluation folder. And for influence maximization problem(IMP), the codes of our ALGE-Greedy algorithm and analysis is provided in influence_maximization folder.

For data, we provide the networks for train and prediction in our work, using csv format with node and edge of netwokrs. The synthetic networks for train are generated by us and attached the influence simulation results for train.

Topology data of real networks is all from Tiago P. Peixoto, "The Netzschleuder network catalogue and repository", https://networks.skewed.de/ (2020). DOI 10.5281/zenodo.7839981. The data is compressed into the dataset's real file, and the reference to each network is listed in the dataset ReadMe. We also present simulation data for methods evaluating. The synthetic topology and labels used for training are in the synthetic folder.

Before performing the calculations, please install the required packages based on the requiements file.

To implement influence evaluation, you can directly run the main_influence_evaluation file. We also provide code for other comparison methods.

To implement influence maximization, you can directly run the main_influence_maximization file. We also provide code for other comparison methods.

Active learning algorithm proposed by us is placed in the Active_learning file.

If you find a problem with the implementation code, please ask under issue or contact us.