Skip to content

kkhuang81/AdaptiveIM

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

20 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Algorithms for Adaptive Influence Maximization

Codes for the EptAIM, i.e., the adaptive algorithm for the adaptive IM problem that provides expected approximation ratio. Please refer to paper: https://arxiv.org/abs/2004.06469 for details. (Notice that this VLDBJ 2020 paper is the latest version of our work on Adaptive IM.)

Running Environment

Linux-based OS

Input requirements

Dataset files: dataset files are referred to $\textit{Tested-Dataset}$ repository.

Realizations files: please refer to $\textit{Tools}$ repository for source code to generate realizations.

Remark: please note that generated realization files are supposed to locate in the same folder of dataset file according to my implementation.

How to run

Running command

./algo -dataset path_to_dataset -model IC -epsilon ε -k seed_number -batch batch_size -seedfile filename -time time_number

Explain

--epsilon: an float number in range (0,1) to control the approximation error.

--k: the number of seed nodes to be selected.

--batch: the size of batch b selected each time.

--seedfile: the file records the k seed nodes selected.

--time: the number of the algorithm repeated.

For example:

./exp_epic -dataset dataset/hep/ -model IC -epsilon 0.5 -k 500 -batch 50 -seedfile seed -time 1

Datasets

Tested datasets can be downloaded in $\textit{Tested-Dataset}$ repository.

Remark

If there are any problems, please contact khuang005@ntu.edu.sg / kkhuang@nus.edu.sg. (https://sites.google.com/view/kekehuang/)

About

Source codes for adaptive influence maximization.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published