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Table 6 Results on influence maximization problems compared to classic methods

From: Gumbel-softmax-based optimization: a simple general framework for optimization problems on graphs

Graph Size Algorithm Number of initial nodes
1 2 3 4 5
Karate 34 Greedy 7.573 \({13.632}\)* \({18.438}\)* \({21.320}\)* \({23.446}\)*
SA 7.573 13.770 18.467 21.843 24.205
GSOa 7.573 13.770 18.467 21.843 24.205
Jazz 198 Greedy 80.283 109.707 125.922 135.396 143.803
SA 80.283 109.707 125.922 135.396 143.803
GSOa 80.283 109.707 125.922 135.396 143.803
Email 1133 Greedy 19.392 34.859 50.322 65.774 81.137
SA 19.392 34.859 50.322 65.774 81.137
GSOa 19.392 34.859 \({50.317}\)* \({65.515}\)* \({80.576}\)*
Government 7057 Greedy 4070.021 4507.093 4725.925 4869.137 5008.638
SA 4070.020 4296.233 4331.295 4375.324 4452.940
GSOa 4070.021 \({4433.256}\)* \({4639.054}\)* \({4788.753}\)* \({4905.063}\)*
  1. The best and the second best results are denoted in italic and asterisk, respectively
  2. aConfiguration: batch size = 128, fixed \(\tau\) = 1, learning rate = 1, \(\alpha\) = 5, instance = 10, and epoch = 2000