How To Use

Code Repository Specification

InfluenceMaximization: algorithms in C# (.NET or Mono is required for compiling the project)

ConfigFilesExamples: configuration examples

data_utils: data preprocessing files in Python

Preprocess Data

1. Data transformation

   data_stat.py: transform the original data from SNAP and get graph statistics information.

   def graph_reform(infile, outfile, head_skip)

2. Seed probability functions assignment

   func_rand_assign.py: assign discounts to users with configurable probability.

   def func_rand_assign(outfile, numnodes)

   The assignments of seed probability functions used in our experiments can be found HERE.

Run Framework

1. Use the examples in ConfigFilesExamples to write a config file.

   The config files we used in our paper can be download HERE.

2. Execute the compiled executable file (e.g., .exe) to read the config file (examples) to run the experiment.

Explanation of Result Files Generated

1. Only AllResults.txt and curve_c(50).txt are used for plotting.

2. AllResults.txt: The first line "Hyper-graph time: 1396278" means building the hyper graph takes 1396278ms.

B=10 IM: 45023.54 6252.5048907138 1403641.2 0.611411976377969 UC: 46404.13 8963.0942677794 1571590.8 CD: 49944.56 7517.24278751192 1585022.4

The above lists results of each algorithm when the budget B=10. For each line, the first number and the second number are influence spread and standard deviation of influence spread estimated by Monte Carlo simulations. The third number is the time costed by the algorithm (in ms). For IM, the fourth number is the approximation lower bound.

3. B=10.txt: "IM 1403641.2 10" means running the influence maximization algorithm takes 1403641.2ms and the algorithm returns 10 users that we should assign non-zero discounts to. Then the follwing 10 lines are like "25084 1 1", which means we should assign 100% (the second number) to user 25084 and the probability user 25084 becomes a seed is 1(the third number).

4. curve_c(50).txt: "0.05 55516.4029581446" means when the unified discount is 0.05, the best influence spread returned by the Unified Discount algorithm is 55516.4029581446.