In this paper, we address the problem of balancing the information exposure in a social network. We assume that two opposing campaigns (or viewpoints) are present in the network, and network nodes have different preferences to- wards these campaigns. Our goal is to find two sets of nodes to employ in the respective campaigns, so that the overall information-exposure balance in the network is maximized.
The folder contains the 8 algorithms, 4 for each setting Heterogeneous and Correlated.
- Greedy_{Heterogeneous,Correlated} (The greedy algorithm that selects to add to a campaign the node that optimizes the objective function.)
- Cover_{Heterogeneous,Correlated} (Cover algorithm, presented in the paper )
- Common_{Heterogeneous,Correlated} (greedy algorithm that only adds common seeds)
- Hedge_{Heterogeneous,Correlated} (Algorithm 2)
To run each algorithm, in the respective folder 'make' and run.
e.g. To select 20 seeds using the Greedy algorithm for the Heterogeneous setting, the command would be:
cd Greedy_Heterogeneous/; make; ./Greedy_Heterogeneous ../datasets/brexit/brexit_network_heterogeneous.txt ../datasets/brexit/side1_seeds.txt ../datasets/brexit/side2_seeds.txt 20
datasets/ contains the 6 Twitter datasets we collected.
- uselections
- brexit
- iphone_samsung
- obamacare
- abortion
- fracking
Each folder contains the following files:
(i) {dataset}network{heterogeneous,correlated}.txt -- the network file, of the format: node1 \t node2 \t side1 probability \t side2 probability (side2 probability doesnt exist for the correlated case). (ii) The seed files side1_seeds.txt, side2_seeds.txt
Contact: Nikos Parotsidis (nikos.parotsidis@uniroma2.it), Kiran Garimella (kiran.garimella@aalto.fi)