saucy3: fast symmetry discovery in graphs hadi katebi karem a. sakallah igor l. markov the...
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Saucy3: Fast Symmetry Discovery in GraphsSaucy3: Fast Symmetry Discovery in Graphs
Hadi KatebiKarem A. SakallahIgor L. Markov
The University of Michigan
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OutlineOutline
Graph symmetry Implicit representation of permutation sets:
Ordered Partition Pairs (OPPs) Basic permutation search tree Pruning via partition refinement:
– Non-isomorphic OPP pruning– Matching OPP pruning
Group-theoretic pruning:– Coset pruning– Orbit pruning
Algorithm trace Experimental results Conclusions
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Graph SymmetryGraph Symmetry
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5 (1 4)(2 3)
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6
4
2
3
1
7
8
5
is a symmetry!
The set of edges is unchanged.
(1 2 3 4)(5 6)is not a symmetry!
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6 5
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6 5
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6The set of edges is different.
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Problem StatementProblem Statement
Given a graph G– with n vertices– and a partition p of its vertices (colors),– with unknown set of symmetries Sym(G)p,
Find a set of symmetries S Sym(G)p
– such that S generates Sym(G)p– and |S| ≤ n - 1
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Graph Symmetry ToolsGraph Symmetry Tools
Nauty (McKay, ’81)– Blazed the trail– Tuned to quickly find the symmetries of
large sets of small graphs Saucy (Darga et al, DAC ’04)
– Graph symmetry can be fast for large yetsparse graphs
– > 1000x speedup over nauty for graphs with tens of thousands of vertices
Bliss (Junttila & Kaski, ’07)– Efficient canonical labeling of sparse graphs– Some improvements on Saucy
Traces (Piperno, ’08)
Primarily
canonical labelin
g tools;
symmetries p
roduced as a byproduct
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PermutationsPermutations
Permutation: bijection from V to V
– Tabular representation– Cycle notation
Graph Symmetry: permutation that preserves edge relation
Permutation Composition: Symmetric group on m-element set T: Sm(T )
– |Sm(T )| = m !
1 2V , , ,n
v v v
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Ordered PartitionsOrdered Partitions
1 2 mW W W
Unit OP: m = 1 Discrete OP: m = n
i i iW V , W
i j i jW W
1 i m iW V
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Ordered Partition Pair (OPP)Ordered Partition Pair (OPP)
1 2
1 2
mT
B k
T T T
B B B
Isomorphic OPP: m = k and
Non-isomorphic OPP: m ≠ k or
Matching OPP: isomorphic and
Unit OPP: top and bottom ordered partitions are unit
Discrete OPP: top and bottom ordered partitions are discrete
for all i iT B i
for all such that 1i i iT B i T
for some i iT B i
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Implicit Representation of Permutations Sets using OPPsImplicit Representation of Permutations Sets using OPPs
2 01021
1 2 0
Discrete OPP(single permutation)
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Implicit Representation of Permutations Sets using OPPsImplicit Representation of Permutations Sets using OPPs
0 1 201 02 12 012 021
0 1 2
, ,, , , , ,
, ,
Unit OPP(m ! permutations)
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Implicit Representation of Permutations Sets using OPPsImplicit Representation of Permutations Sets using OPPs
2 0 112 021
1 2 0
,,
,
Isomorphic OPP
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31 0 2 4 3
13 0 2 43 0 2 4 1
, ,S , ,
, ,
Implicit Representation of Permutations Sets using OPPsImplicit Representation of Permutations Sets using OPPs
Matching OPP
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Implicit Representation of Permutations Sets using OPPsImplicit Representation of Permutations Sets using OPPs
0 21
12 0
,
,
Non-isomorphic OPP
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Basic Search for SymmetriesBasic Search for Symmetries
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, ,
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Vertex Partition RefinementVertex Partition Refinement
For each vertex v, compute a neighbor-count tuple Partition the vertices based on these tuples Repeat until the partition stabilizes
Try to distinguish vertices that are not symmetric
1 2 3 4 6 7 8
1 2 3 54 6 7 8
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(2,1)(2,1) (2,1)
(2,1)
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Search-Tree Pruning via Vertex RefinementSearch-Tree Pruning via Vertex Refinement
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Isomorphic RefinementIsomorphic Refinement
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Top Bottom
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Non-Isomorphic RefinementNon-Isomorphic Refinement
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Group GeneratorsGroup Generators
8 vertices: 8! = 40320 permutations
48 symmetries8 for square and 6 for triangle
Basic enumeration is inefficient Fundamental concept: symmetry group can be represented
implicitly by an exponentially smaller set of generators
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4
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5
identity(1 2 4 3)(1 4)(2 3)(1 3 4 2)(1 2)(3 4)(1 3)(2 4)(1 4)(2 3)
identity(6 7 8)(6 8 7)
(6 7)(6 8)(7 8)
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Group GeneratorsGroup Generators
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1
3
2
4
7
8
5
Generators:
g1 = (1 2)(3 4)
g2 = (2 3)
g3 = (6 7)
g4 = (6 8)
(1 3 4 2)(7 8) = g2g1g3g4g3
is a symmetry
g4g3 = (6 8) ◦ (6 7) = (6 7 8)
g3g4g3 = (6 7) ◦ (6 7 8) = (7 8)
g1g3g4g3 = (1 2)(3 4) ◦ (7 8) = (1 2)(3 4)(7 8)
g2g1g3g4g3 = (2 3) ◦ (1 2)(3 4)(7 8) = (1 3 4 2)(7 8)
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Orbit PartitionOrbit Partition
Initial partition:{{1,2,3,4,5,6,7,8}}
Orbit partition:{{1,2,3,4},{5},
{6,7,8}} After degree refinement:
{{1,2,3,4,6,7,8},{5}}
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8
5
Symmetry group induces an equivalence relation on vertices:the orbit partition
Refinement provides an approximation of the orbit partition Orbit partition:
– Built up incrementally from discovered symmetries– Used to prune search for redundant symmetries
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CosetsCosets
A subgroup H of a group G partitions it into cosets Each coset has the same number of elements as
H G can be generated by composing a single
representative from each coset with H Used to prune search for redundant symmetries
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Structure of Permutation Search TreeStructure of Permutation Search Tree
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Algorithm OutlineAlgorithm Outline
Phase 1: Recursive subgroup decomposition Phase 2: Search for coset representatives …
surprisingly like SAT solving! Four pruning mechanisms:
– Group-theoretic Coset pruning: stop after coset representative is
found Orbit pruning: avoid looking for coset
representative– Algorithmic (due to OPP data structure):
Matching OPP pruning: identify candidate permutation before reaching leaves
Non-isomorphic OPP pruning: detect absence of coset representative in current subtree
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Saucy 2.1 Search TreeSaucy 2.1 Search Tree
≈
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2
4
7
85
{7,8}
{6,7,8}
{2,3}{6,7,8}
{1,2,3,4}{6,7,8}
Orbit Partition
1,2,3,4,6,7,8 5
1,2,3,4,6,7,8 5
2,3,4,6,7,8 1 5
2,3,4,6,7,8 1 5
6,7,8 4 2,3 1 5
6,7,8 4 2,3 1 5
6,7,8 4 3 2 1 5
6,7,8 4 3 2 1 5
7,8 6 4 3 2 1 5
7,8 6 4 3 2 1 5
2,3,4,6,7,8 1 5
1,3,4,6,7,8 2 5
6,7,8 4 2,3 1 5
6,7,8 3 1,4 2 5
6,7,8 4 3 2 1 5
6,7,8 4 2 3 1 5
7,8 6 4 3 2 1 5
6,8 7 4 3 2 1 5
(7 8)id (6 7)
6,7,8 4 3 2 1 5
6,7,8 3 4 1 2 5
2,3,4,6,7,8 1 5
1,2,3,4,7,8 6 5
6,7,8 | 4 2,3 1 5
1,2,3,4 7,8 6 5
//
≈
// //x
(2 3)=
(1 2)(3 4)=
≈ Coset pruning// Orbit Pruning= Matching OPPx Non-isomorphic OPP
11 121314
16
1718
22 23
66 67 68
21 24
77 78 76 78
R R R
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Experimental EvaluationExperimental Evaluation
1183 SAT 2009 competition benchmarks– Application– Crafted – Random
Saucy on all 1183 Shatter on 47 most difficult benchmarks Experiments on SUN workstation
– 3GHz Intel Dual-Core CPU– 6MB cache– 8GB RAM– 64-bit Redhat Linux
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Saucy Time vs. Graph VerticesSaucy Time vs. Graph Vertices
0.001
0.01
0.1
1
10
100
1000
1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06 1.E+07 1.E+08
Graph Vertices
Tim
e (s
)
Crafted Application Random
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Saucy Time vs. Graph VerticesSaucy Time vs. Graph Vertices
Time out = 500 sec. Sacuy finished on all but 18
– connum: 6 (solved by varying branching heuristics)
– equilarge: 3– mod2-rand3bip: 9
Crafted category is the most challenging Weak trend towards larger run times for larger
graphs Saucy is really fast (runtime < 1 sec.) on 93%
(1101) of all benchmarks
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Saucy Group Order vs. TestcaseSaucy Group Order vs. Testcase
1.E+00
1.E+06
1.E+12
1.E+18
1.E+24
1.E+30
1.E+36
1.E+42
1.E+48
1.E+54
1.E+60
0 50 100 150 200 250 300
Testcase
Gro
up O
rder
Crafted Application Random
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Saucy Group Order vs. TestcaseSaucy Group Order vs. Testcase 323 benchmarks exhibited non-trivial symmetries Random category:
– 606 had no symmetry– 4 had one symmetry
Crafted category:– 175 out of 263 (66%) had symmetry– 18 timed out
Application category:– 144 out of 292 (50%) had symmetry
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Saucy Group Order vs. #generatorsSaucy Group Order vs. #generators
1.E+00
1.E+35
1.E+70
1.E+105
1.E+140
1.E+175
1.E+210
1.E+245
1.E+280
1 10 100 1000
# Generators
Gro
up O
rder
Crafted Application Random
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Saucy Group Order vs. #generatorsSaucy Group Order vs. #generators Guarantee to produce no more than n - 1
generators for n-vertex graph The number of reported generators is significantly
less than n - 1
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Shatter +SBP VariablesShatter +SBP Variables
1.E+01
1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E+07
1.E+01 1.E+03 1.E+05 1.E+07Original Variables
+SB
P V
aria
bles
Crafted Application
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Shatter +SBP ClausesShatter +SBP Clauses
1.E+01
1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E+07
1.E+01 1.E+03 1.E+05 1.E+07
Original Clauses
+SB
P C
laus
esCrafted Application
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Shatter Variables and ClausesShatter Variables and Clauses
Shatter on 47 benchmarks – Unsolved benchmarks or benchmarks with
run time > 1000 sec. Application: 13 Crafted: 34
# added SBP clauses – Less than 4% for 29 benchmarks– Ranged from 25% to 133% for 18 benchmarks
# added SBP variables– Less than 1% for 23 benchmarks– Ranged from 9% to an order of magnitude for
24 benchmarks
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Shatter (Symmetry-Breaking) FlowShatter (Symmetry-Breaking) Flow Use shatter to generate SBPs Add SBPs to the original CNF formula Pass the augmented CNF formula to the SAT
solver Statistical data:
– We used a re-ordering script to Reorder variables Reorder clauses
– 20 re-ordered versions of each benchmark 10 for the original benchmarks 10 for the SBP augmented benchmarks
Time-outs– Crafted: 5000 sec.– Application: 10000 sec.
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SAT Solver Run Time SAT Solver Run Time
0100020003000400050006000700080009000
10000
0 1 2 3 4 5 6 7 8 9 10 11 12 13Benchmark
Tim
e (s
)
Original +SPB
1 2 3 4 5 6 7 8 9 10 11 12 13
1. mod3block_4vars_11gates_b2_restricted2. mod4block_2vars_8gates_u23. phnf-size10-exclusive-FIFO.used-as. sat04-991.sat05-4192.reshuffled-074. sgp_5-5-6.sat05-2675.reshuffled-075. sgp_5-6-8.sat05-2669.reshuffled-076. 9dlx_vliw_at_b_iq57. 9dlx_vliw_at_b_iq68. 9dlx_vliw_at_b_iq79. 9dlx_vliw_at_b_iq810. 9dlx_vliw_at_b_iq911. clauses-812. cube-11-h14-sat13. post-cbmc-aes-ee-r3-noholes
5 1 9
5
10
5
2
1
6 5
99
1
35
4
5 5
13 out of 47 benchmarked finished within time-outs
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SAT Solver Run TimeSAT Solver Run Time
SBP augmented versions led to fewer time-outs All but 3 benchmarks were solved faster Four benchmarks which were reported to be
unsolvable in SAT 2009 competition were solved with the addition of SBPs
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Conclusions and Future WorkConclusions and Future Work
For SAT: symmetry discovery is practically free Static symmetry breaking
– Many CNF instances possess no or little symmetry
– CNF instances with a lot of symmetry may or may not benefit from static symmetry breaking
Future work:– SAT-inspired algorithmic enhancements:
Branching heuristics Learning
– Dynamic symmetry breaking: Integrating symmetry breaking within the SAT
solver Uncovering hidden/conditional symmetries