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COUNTING SUBGRAPHS COUNTING SUBGRAPHS

COUNTING SUBGRAPHS - PowerPoint Presentation

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COUNTING SUBGRAPHS - PPT Presentation

Radu Curticapean Holger Dell Dániel Marx NEW INSIGHTS INTO Saarland University Cluster of Excellence MMCI Institute for Computer Science and Control Hungarian Academy of ID: 544158

counting time hard homomorphisms time counting homomorphisms hard subgraphs count vertices poly matching treewidth clique eth bounded linear combinations 341 injective parameter

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Slide1

COUNTING SUBGRAPHS

Radu Curticapean, Holger Dell, Dániel Marx

NEW INSIGHTS INTO

Saarland University,Cluster of Excellence (MMCI)

Institute for Computer Science and Control,Hungarian Academy of Sciences (MTA SZTAKI)Slide2

vertices

 

pattern

 

vertices

 

h

ost

 

count

subgraphs

F

of

G

isomorphic

to H

NOT NECESSARILY INDUCED

COUNTING SUBGRAPHSSlide3

-clique

 

-

cycle

 

-

matching

 

-star

 

with

vtx

-cover

of

size

 

any

 

 

[NP 85]

 

[BKK 14]

 

[BKK 14]

 

#W[1]-hard

refutes ETH

[CCFHJKX 04]

 

#W[1]-hard

[FG 04]

#W[1]-hard,

refutes ETH

 

 

 

vertices

 

pattern

 

vertices

 

h

ost

 

count

subgraphs

F

of

G

isomorphic

to

H

[BHKK 09]

 

[C 13, CM 14, CM 15]

ETH:

SAT on

-variable formulas

has no

time algorithm

 

parameterized problem X:

input comes with some parameter

X is FPT:

problem can be solved in time

is #W[1]-hard:

FPT-algorithm for #

-clique with

-oracle

and

all queries have parameter

for some

X most likely not FPT

 Slide4

vertices

 

pattern

 

vertices

 

h

ost

 

count

subgraphs

F

of

G

isomorphic

to H

poly-time

#W[1]-hard.

with vtx

-coverof

size

 

-star

 

-clique

 

-

cycle

 

-

matching

 Slide5

poly-time

#W[1]-hard

.

Input:

Graphs

and

Parameter:

Output:

 

maximum matching in

 

bounded

for

recursively

enumerable

 

unbounded

with

vtx

-cover

of

size

 

-star

 

[CM 14]

-clique

 

-

cycle

 

-

matching

 

classifies

w

is

poly

-time

assuming

 

despite

-intermediate cases!

[CTW 08]

 Slide6

poly-time

-

hard

 

finite

 

count

/find

induced

subgraphs

from

 

else

[CTW 08]

count/find

colorful subgraphs

from

 

has bounded

treewidth

 

else

[GSS 01][CM 14]

count

homomorphisms from

 

has bounded

treewidth

 

else

[G 07]

[DJ 04]

count

subgraphs from

 

has bounded

matchings

 

else

[CM 14]

tree-decomposition of H:

tree

with bags

at

Each

is in some bag

Each

is in some bag

If

appears in bags of

,

then

is connected in

width of

: max. bag size

treewidth of

: min. width over all

 

David Eppstein, Wikipedia

 

 Slide7

 

 

COUNTING

HOMOMORPHISMS

poly-time

-

hard

 

count

homomorphisms

from

 

has bounded

treewidth

 

else

[G 07]

[DJ 04]

 

time

via standard D

 Slide8

COUNTING

SUBGRAPH EMBEDDINGS

 

 

 

 

 

 

 

COUNTING

HOMOMORPHISMSSlide9

 

 

 

 

 

 

COUNTING

HOMOMORPHISMS

COUNTING

INJECTIVE HOMOMORPHISMSSlide10

 

 

 

 

 

 

COUNTING

HOMOMORPHISMS

COUNTING

INJECTIVE HOMOMORPHISMS

COUNTING LINEAR

COMBINATIONS OF

COUNTING LINEAR

COMBINATIONS OF

 Slide11

 

1

2

3

4

Define

for

partition

of

Contract every block of

in

.

Delete multi-

edges

,

keep

self

-loops.

 

12

3

4

12|3|4

34|1|2

341|2

1234

1|2|3|4

13|2|4

14|2|3

23|1|4

24|1|3

12|34

13|24

14|23

23|14

24|13

34|12

123|4

142|3

234|1

LINEAR COMBINATIONS OF

HOMOMORPHISMS

 

COUNTING

INJECTIVE HOMOMORPHISMS

 

Spasm

 

1

2

34

2

341

1

2

3

4Slide12

1|2|3|4

12|3|4

13|2|4

14|2|3

23|1|4

24|1|3

34|1|2

12|34

13|24

14|23

23|14

24|13

34|12

123|4

142|3

234|1

341|2

1234

 

1

2

3

4

LINEAR COMBINATIONS OF

HOMOMORPHISMS

 

COUNTING

INJECTIVE HOMOMORPHISMS

 

 

 

12

3

4

1

2

34

2

341

1

2

3

4Slide13

 

 

First

application

:

Counting

any

-

edge

subgraph

in time

 

best

known

upper

bound

on

treewidth

of

-

edge

graph

 

-

paths

(

before

: time

)

-

matchings

(

before

: time

)

 

Lovász

1967Slide14

Scott, Sorkin 2016

:

Graphs

with

edges have

 

 

 

time

 

poly

time

graphs,

each

with

edges

 Slide15

Second application:

Hardness of counting subgraphs

via fine-grained reductions

 

 Slide16

 

 

 

 

#

PartitionedSub

(

 

Pick

any

.

 

Given

oracle

for

this

,

we

can

compute

this

in

steps.

 Slide17

 

 

 

 

#

PartitionedSub

(

 

under #ETH

[Marx 2010]

even when fixing

and allowing only

 

under #ETH

even when fixing

and allowing only

 

Pick

any

.

 

Given

oracle

for

this

,

we

can

compute

this

in

steps.

 

has

-

matching

pick expander

with

 Slide18

always in time

.

Exact value of

for

fixed ?

 

 

 

 

 

exponent in running time

for #PartitionedSub

 

exponent in running time

for computing #H

 

Can

you

beat

treewidth

:

Clique

conjecture

:

if

 

Lovász

1967