python dcop_gen_rand.py --agts 50 --doms 10 --p1 1.0 --p2 0.5 --max_arity 2 --max_cost 100 --name test --ofile test
Parameters:
agts (int) – the number of nodes
doms (int) – the size of the domains
p1 (float) – the constraint graph density (0 = disconnected, 1 = fully connected)
p2 (float) – the constraint tightness (0 = full soft, 1 = full hard)
max_arity (int) – the maximum constraint arity
max_cost (int) - the maximum cost of a constraint value
name (str) the name of the instance
ofile (str) path to the output files
Generates a Random instance according to the above parameters
python dcop_gen_grid.py --agts 50 --doms 10 --p2 0.5 --max_arity 2 --max_cost 100 --name test --ofile test
Parameters:
agts (int) – the number of nodes along 1 side of the grid-graph
doms (int) – the size of the domains
p2 (float) – the constraint tightness (0 = full soft, 1 = full hard)
max_arity (int) – the maximum constraint arity
max_cost (int) - the maximum cost of a constraint value
name (str) the name of the instance
ofile (str) path to the output files
Generate a planar grid graph whose nodes is agts^2
python dcop_gen_scalefree.py --agts 50 --doms 10 --m 4 --t 0.3 --p2 0.5 --max_arity 2 --max_cost 100 --name test --ofile test
Parameters:
agts (int) – the number of nodes
doms (int) – the size of the domains
m (int) – the number of random edges to add for each new node
t (float) – Probability of adding a triangle after adding a random edge
p2 (float) – the constraint tightness (0 = full hard, 1 = full soft)
max_arity (int) – the maximum constraint arity
max_cost (int) - the maximum cost of a constraint value
name (str) the name of the instance
ofile (str) path to the output files
Holme and Kim algorithm for growing graphs with powerlaw degree distribution and approximate average clustering.
The average clustering has a hard time getting above a certain cutoff that depends on m. This cutoff is often
quite low. The transitivity (fraction of triangles to possible triangles) seems to decrease with network size.
It is essentially the Barabási–Albert (BA) growth model with an extra step that each random edge is followed by
a chance of making an edge to one of its neighbors too (and thus a triangle).