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multi_advertiser_influence's Introduction

Code for greedy and LP rounding based seed set selection in a mult-advertiser scenario. Author - Kartik Lakhotia

Directory structure

  1. greedy - greedy algorithm
  2. lpSol - LP rounding based algorithm
    Uses CPLEX as an LP solver and python PULP interface for creating the LP

To run a particular algorithm, go to the corresponding directory and use following commands.

Compile

make

Run the code

./imm_greedy -dataset *** -epsilon *** -k ***  -h ***"

k is the total seed set size for all advertisers
h is the max number of advertisements per user

Input dataset

Directory containing the following files:
    
    attribute.txt - specify problem parameters
        n - number of vertices
        m - number of edges
        a - number of advertisers
        g - number of influence networks (either 1 or a)

    budgets.txt - max budget for each advertiser

    graph_ic.inf - edge list

    prob<i> - edge probabilities for each influence network

Reference Dataset

For reference, checkout the ./testGraph/ directory

Acknowledgements

This code was built on top of the single greedy influence maximization code available at\
https://sourceforge.net/projects/im-imm/

multi_advertiser_influence's People

Contributors

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Watchers

James Cloos avatar Kartik Lakhotia avatar

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