Gset is the benchmark of Max-cut problem which is one of the combinatorial optimization problems.
Cited from : here
If you generate adjacency matrix, execute python file generate_graph.py.
python3 generate_graph.pyGset has 71 instances. The bellow is cited from here
| instance | #vertices | #edges | Weight | Best known |
|---|---|---|---|---|
| G1 | 800 | 19,176 | +1 | 11,624 |
| G2 | 800 | 19,176 | +1 | 11,620 |
| G3 | 800 | 19,176 | +1 | 11,622 |
| G4 | 800 | 19,176 | +1 | 11,646 |
| G5 | 800 | 19,176 | +1 | 11,631 |
| G6 | 800 | 19,176 | +1, -1 | 2,178 |
| G7 | 800 | 19,176 | +1, -1 | 2,006 |
| G8 | 800 | 19,176 | +1, -1 | 2,005 |
| G9 | 800 | 19,176 | +1, -1 | 2,054 |
| G10 | 800 | 19,176 | +1, -1 | 2,000 |
| G11 | 800 | 1,600 | +1, -1 | 564 |
| G12 | 800 | 1,600 | +1, -1 | 556 |
| G13 | 800 | 1,600 | +1, -1 | 582 |
| G14 | 800 | 4,694 | +1 | 3,064 |
| G15 | 800 | 4,661 | +1 | 3,050 |
| G16 | 800 | 4,672 | +1 | 3,052 |
| G17 | 800 | 4,667 | +1 | 3,047 |
| G18 | 800 | 4,694 | +1, -1 | 992 |
| G19 | 800 | 4,661 | +1, -1 | 906 |
| G20 | 800 | 4,672 | +1, -1 | 941 |
| G21 | 800 | 4,667 | +1, -1 | 931 |
| G22 | 2,000 | 19,990 | +1 | 13,359 |
| G23 | 2,000 | 19,990 | +1 | 13,344 |
| G24 | 2,000 | 19,990 | +1 | 13,337 |
| G25 | 2,000 | 19,990 | +1 | 13,340 |
| G26 | 2,000 | 19,990 | +1 | 13,328 |
| G27 | 2,000 | 19,990 | +1, -1 | 3,341 |
| G28 | 2,000 | 19,990 | +1, -1 | 3,298 |
| G29 | 2,000 | 19,990 | +1, -1 | 3,405 |
| G30 | 2,000 | 19,990 | +1, -1 | 3,413 |
| G31 | 2,000 | 19,990 | +1, -1 | 3,310 |
| G32 | 2,000 | 4,000 | +1, -1 | 1,410 |
| G33 | 2,000 | 4,000 | +1, -1 | 1,382 |
| G34 | 2,000 | 4,000 | +1, -1 | 1,384 |
| G35 | 2,000 | 11,778 | +1 | 7,687 |
| G36 | 2,000 | 11,766 | +1 | 7,680 |
| G37 | 2,000 | 11,785 | +1 | 7,691 |
| G38 | 2,000 | 11,779 | +1 | 7,688 |
| G39 | 2,000 | 11,778 | +1, -1 | 2,408 |
| G40 | 2,000 | 11,766 | +1, -1 | 2,400 |
| G41 | 2,000 | 11,785 | +1, -1 | 2,405 |
| G42 | 2,000 | 11,779 | +1, -1 | 2,481 |
| G43 | 1,000 | 9,990 | +1 | 6,660 |
| G44 | 1,000 | 9,990 | +1 | 6,650 |
| G45 | 1,000 | 9,990 | +1 | 6,654 |
| G46 | 1,000 | 9,990 | +1 | 6,649 |
| G47 | 1,000 | 9,990 | +1 | 6,657 |
| G48 | 3,000 | 6,000 | +1, -1 | 6,000 |
| G49 | 3,000 | 6,000 | +1, -1 | 6,000 |
| G50 | 3,000 | 6,000 | +1, -1 | 5,880 |
| G51 | 1,000 | 5,909 | +1 | 3,848 |
| G52 | 1,000 | 5,916 | +1 | 3,851 |
| G53 | 1,000 | 5,914 | +1 | 3,850 |
| G54 | 1,000 | 5,916 | +1 | 3,852 |
| G55 | 5,000 | 12,498 | +1 | 10,299 |
| G56 | 5,000 | 12,498 | +1, -1 | 4,017 |
| G57 | 5,000 | 10,000 | +1, -1 | 3,494 |
| G58 | 5,000 | 29,570 | +1 | 19,293 |
| G59 | 5,000 | 29,570 | +1, -1 | 6,086 |
| G60 | 7,000 | 17,148 | +1 | 14,188 |
| G61 | 7,000 | 17,148 | +1, -1 | 5,796 |
| G62 | 7,000 | 14,000 | +1, -1 | 4,870 |
| G63 | 7,000 | 41,459 | +1 | 27,045 |
| G64 | 7,000 | 41,459 | +1, -1 | 8,751 |
| G65 | 8,000 | 16,000 | +1, -1 | 5,562 |
| G66 | 9,000 | 18,000 | +1, -1 | 6,364 |
| G67 | 10,000 | 20,000 | +1, -1 | 6,950 |
| G70 | 10,000 | 9,999 | +1 | 9,591 |
| G72 | 10,000 | 20,000 | +1, -1 | 7,006 |
| G77 | 14,000 | 28,000 | +1, -1 | - |
| G81 | 20,000 | 40,000 | +1, -1 | - |