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discovering_influencers_social_interactions_icassp2023_ojsp2023

The data and the code for the experiments in the papers:

V. Shumovskaia, M. Kayaalp, M. Cemri, and A. H. Sayed, “Discovering influencers in opinion formation over social graphs,” IEEE Open Journal of Signal Processing, pp. 1–20, 2023.DOI

V. Shumovskaia, M. Kayaalp, and A. H. Sayed, “Identifying opinion influencers over social networks,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pp. 1–5, 2023.DOI

To get the figures from the paper, run these scripts:

python plot_draft.py

To fully run experiments, we refer to runs_final.txt, e.g.:

python main.py --likelihood_regime 0 --times 70000 --adjacency_regime 3 --agents 20 --er_prob 0.2 --seed 21 --lr 0.1 --step_size 0.05 --likelihood_regime 3 --multistate --states 5 --comparison --window 50

To understand arguments for the parser we refer to parser.py or run python main.py --help.

Find experiments on Twitter data in the following repo asl-epfl/discovering_influencers_twitter_ojsp2023.

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