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Table 5 Results on MIS and MVC problems compared to classic methods and supervised deep learning methods.\(^{1}\)

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

  Graph Info Classic Supervised Proposed
Name Size MD-Greedy Greedy SA S2V-DQN GCNGTS GSOa/EvoGSOb
MIS Cora 2708 1451 672 1390 1381 1451 \({1443}\)*
Citeseer 3327 \({1818}\)* 1019 1728 1705 1867 1795
PubMed 19,717 15,912 5353 14,703 15,709 15,912 \({15,886}\)*
MVC Cora 2708 1257 2036 1318 1327 1257 \({1265}\)*
Citeseer 3327 \({1509}\)* 2308 1599 1622 1460 1533
PubMed 19,717 3805 14,364 5014 4008 3805 \({3831}\)*
  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 = 0.01, \(\alpha\) = 3, and instance = 20
  3. bConfiguration: batch size = 512, fixed \(\tau\) = 1, learning rate = 0.01, \(\alpha\) = 3, instance = 20, cycle \(T_1\) = 100, substitution ratio 1/u = 1/8, cycle \(T_2\) = 10,000, mutation rate m = 0.001, and elite ratio = 0.0625