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Page 1: Network Identifiability  with  Expander Graphs

Network Identifiability with

Expander Graphs

Hamed Firooz, Linda Bai, Sumit Roy

Spring 2010

Page 2: Network Identifiability  with  Expander Graphs

Outline Identifiability definition Identifiability using graph theory

(Linda) Identifiability using expander

graph

Page 3: Network Identifiability  with  Expander Graphs

Definition of Identifiability

Page 4: Network Identifiability  with  Expander Graphs

Network Tomography

Network?

Given a network, and a limited number of end-hosts, can we infer what’s happening inside the network

Here our goal is to find the links delay

Page 5: Network Identifiability  with  Expander Graphs

End1

End2 End3

router1

link1

link2

link3 110321013101121

321

EndEndEndEndEndEnd

linklinklink

Routing matrix R

Delay TomographyUsing probes that are inserted into a data stream, end-to-end properties on that route can be measured.

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4

3

2

1

54321

11000101100001101101

PPPP

R

lllll

y=Rx

Delay Tomography

5

4

3

2

1

l

l

l

l

l

ddddd

x

4

3

2

1

P

P

P

P

dddd

y4311 lllP dddd

1 2

3

4 5

P1

We are interested in Links delay

Page 7: Network Identifiability  with  Expander Graphs

Problem: predict or estimate x from y with y = Rx

R (N-by-M matrix) : binary routing matrixX (M-by-1 vector) : quantity of interest, e.g, link delayY (N-by-1 vector) : known aggregations of X (measurements) [3]

Identifiability: a network is identifiable if y=Rx has unique solution [5]

• Usually, M ( # of links in network) >> N (# of measurements) so network is generically NOT identifiable.

Deterministic Model

Page 8: Network Identifiability  with  Expander Graphs

k-identifiability a network is identifiable if y=Rx has

unique solution Since this is an underdetermined system

of equations, it doesn’t have unique answer

We need side information: k-identifiability: delay of up to k links which

are significantly higher than the others can be inferred from end-to-end measurement y=Rx

significantly higher makes vector x k-sparse (k-compressible)

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1-identifiability Delay from End1 to End2 is

d1+d2

It is impossible to figure out the delay of each link

In fact, there is no difference between 1 and 2 in end-to-end measurement

`

`

l1

l2

End1

End2

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1-identifiable A graph which has an intermediate node

with degree 2 is not 1-identifiable In general, a graph is not 1-identifiable

if and only if:

In each end-to-end delay measurement we either have the term d1+d2 or we don’t have d1 nor d2

PlPlEll 2121,

N1 N2l1 l2

Page 11: Network Identifiability  with  Expander Graphs

1-identifiable Let’s look at routing matrix

Above statement means: if you look at columns corresponding to 1 and 2 they are both zero or one there is two identical columns

11110011

4321

............ 21

PPPP

ll

PlPlEll 2121,

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k-identifiable Graph with a node (intermediate) which

has degree k+1 is not k-identifiable. If graph is i-identifiable it is j identifiable

if j<i

Main question: given the routing matrix of a network , is it k-identifiable?

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k-identifiable If a graph is k-identifiable then each k+1

columns of its routing matrix are independent (necessary condition)

Is this a sufficient condition? If every 2k columns of R are independent

then graph G is k-identifiable if k=1 then k+1=2k=2 so identical

columns gives necessary and sufficient conditions for 1-identifiability

Page 14: Network Identifiability  with  Expander Graphs

Expander Graphs

Page 15: Network Identifiability  with  Expander Graphs

Bipartite Graph A graph G(V,E) is called bipartite if:

Usually G(V1,V2,E) V1 is left part, V2 is rightpart

EwvVwvEwvVwvVVtsVVV

),(,),(,

..

2

1

2121

V1 V2

Page 16: Network Identifiability  with  Expander Graphs

Bi-adjacency matrix Adjacency matrix A=[aik], aik=1 iff

node i is connected to node k Bi-adjacency matrix T=[tik], tik=1

iff node i in V1 is connected to node k in V2

V1 V2

110001011001

T

00tTT

A

Page 17: Network Identifiability  with  Expander Graphs

Regular Graph A graph G(V,E) is called d-regular if

deg(v)=d for all v in V A bipartite graph G(V1,V2,E) is

called left d-regular if for all v in V1 deg(v)=d

Number of ones in each row is d

V1V2

110101011011

T

Page 18: Network Identifiability  with  Expander Graphs

Expander graph Let Let N(S) be set of neighbors of X in V2 G(V1,V2,E) is called (s,ɛ)-expander if

Each set of nodes on the leftexpands to N(S) number of nodesOn right

1VS

||)1(|)(|||,1 SdSNsSVS

V1 V2

Page 19: Network Identifiability  with  Expander Graphs

Expander graph

V1 V2 V1 V2 V1 V2

V1 V2 V1 V2 V1 V2

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Expander & Compressed Sensing

Let G(V1,V2,E) be a (2k,ɛ)-expander with left degree d

Let R=Tt

two vectors x and x’ have the same projection under measurement matrix R; i.e. Rx = Rx’

Suppose Then S: set of k largest coefficients of x

||||)(|||| 1 cSxfxx

11 |||||||| xx

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Routing Matrix & Bipartite Let Network N(V,E) is given with

end to end set of paths P The routing matrix R is a |P|-by-|E|

binary matrix It can be considered as bi-

adjacency matrix of a bipartite graph G(E,P,H)

Page 22: Network Identifiability  with  Expander Graphs

Example Routing matrix `

`

l1

l2

End1

End2

`

`

End3

End4

l3

l4l5

11000000111011001101

4

3

2

1

54321

PPPP

R

lllll

P1 P2

P3

P4

Page 23: Network Identifiability  with  Expander Graphs

Example This is a bipartite graph with

biadjacency matrix Rt

Is this an expander?l1

l2

l3

l4

l5

P1

P2

P3

P4

Page 24: Network Identifiability  with  Expander Graphs

Example This is (2,1/4)-expander with

left degree 2:

If |X|=1, since degree eachnode is 2|N(X)|=2>1.5

l1

l2

l3

l4

l5

P1

P2

P3

P4

||5.1||243|)(|2||, XXXNXVX

Page 25: Network Identifiability  with  Expander Graphs

Example This is (2,1/4)-expander with

left degree 2:

If |X|=1, since degree eachnode is 2|N(X)|=2>1.5 If |X|=2, it can be provedThat |N(X)|=3=1.5*2=3

l1

l2

l3

l4

l5

P1

P2

P3

P4

||5.1||243|)(|2||, XXXNXVX

Page 26: Network Identifiability  with  Expander Graphs

1-identifiability N(V;E) a network with paths collection P

and routing matrix R. G(E;P;H) is a bipartite graph with

biadjacency matrix R. x* is delay vector of N(V;E). x is a solution to the LP optimization:

then if G is a (2;d;ɛ)-expander with

*

1

s.t.||||min

RxRx

x

||||)(|||| 1

*cS

xfxx 41

Page 27: Network Identifiability  with  Expander Graphs

reverse of Theorem is not true This network is 1-identifiable Bipartite graph coressponding

to R is not regular 1

2 3

45

6

111000100101001011

R

Page 28: Network Identifiability  with  Expander Graphs

It contains two expander-subgraphs

N(V;E) network with routing matrix R

G(X; Y;H) bipartite graph with bi-adjacency R

Gi(Xi;Y;Hi), i = 1; 2; …M is di-regular N is 1-identifiable if each Gi is an expander

jiddHHXX jiii ,,

Page 29: Network Identifiability  with  Expander Graphs

Expansion parameter In conclusion, graph G(V,E) is k-identifiable

with routing matrix R, if R is bi-adjacency matrix of a (2k, ɛ)-expander graph

There are lots of paper on how to construct an expander (Used for design measurement matrix)

Given a bipartite graph, what is its expansion parameter? There is no known theorem

We solve this problem for (2,ɛ)-expander; i.e. 1-identifiable

Page 30: Network Identifiability  with  Expander Graphs

G(V,E) is a graph with adjacency matrix H

Entry (i,j) of H2 gives number of walks with length 2 from node i to node j

0010001111010110

H1

2

3 4

1101121101311112

2H

Page 31: Network Identifiability  with  Expander Graphs

2-expander In a bipartite graph entry (i,j) of TtT

gives number of walks with length 2 from a node V1 to another node in V1

In a bipartite graph entry (i,j) of TtT presents number of common neighbors of nodes i and j.

TTTT

TT

TT

Ht

t

tt 00

00

.0

02

Page 32: Network Identifiability  with  Expander Graphs

Example TtT shows that each two node have

at most 1 node in common Each node has 2 neighbors this is (2,1/4)-expander

l1

l2

l3

l4

l5

P1

P2

P3

P4

2111012101112111012101112

TT t

3|)(|2||,1 SNSVS

Page 33: Network Identifiability  with  Expander Graphs

Theorem A bipartite graph G(V1,V2,E), with left

degree d, is (2,1/4)-expander if

Doesn’t have any negative entry In conclusion, a graph G(V,E) with routing

matrix A is 1-identifiable if

Doesn’t have any negative entry

TTJd t*2

11

11

J

tAAJd*

2

Page 34: Network Identifiability  with  Expander Graphs

Theorem A bipartite graph G(V1,V2,E), with left

degree d, is (2, ɛ)-expander if

Doesn’t have any negative entry In conclusion, a graph G(V,E) with routing

matrix R is 1-identifiable if

Doesn’t have any negative entry

TTJd t2

11

11

J

tRRJd 2

Page 35: Network Identifiability  with  Expander Graphs

Best paths There are actually 6 paths

inside the network Obviously only 4 of them are

sufficient to figure out delay of every link inside the network.

Question is how to select those path? End-to-end delay measurements

using probe transmission compels extra burden on the network

Minimize cost of identifiability

P1P2

P3

P4

P5P6

Page 36: Network Identifiability  with  Expander Graphs

Graph Covering Suppose G(V,E) is given with set of

paths P Question: Select a subset of P such that

every link in G belong to at least one of the paths

Minimum number of paths that make a link failure inside the network detectable

Is there any congested link inside the network

Page 37: Network Identifiability  with  Expander Graphs

Indicator function

Goal is to minimize number of paths:

Subject to each link belong to at least one path

link L1: Number of paths go through it:

N

iPiI

1

min

P1P2

P3

P4

P5P6

1531 PPP III

o.w.0used is 1 i

P

PI

i 11

Page 38: Network Identifiability  with  Expander Graphs

IP=[IP1, IP2,…, IPN] In general, ith entry of Rt .IP gives

number of paths go through link i To cover all links

component-wise

1Pt IR

}1,0{1..

min1

i

i

P

Pt

N

iP

IIRts

I

Page 39: Network Identifiability  with  Expander Graphs

We know graph is 1-identifiable if R is the bi-adjacency matrix of an 2-expander graph

The condition is 0))(deg(

23|)(| SSN

Page 40: Network Identifiability  with  Expander Graphs

These are Binary Integer Programming We can solve the LP version and select

the highest IPi

}1,0{

04

1..

min

,::

1

i

jkjij

j

jkjij

j

i

P

PlPlPP

PlorPlPP

Pt

N

iP

I

kiII

IRts

I

Page 41: Network Identifiability  with  Expander Graphs

}1,0{

1..

min1

i

i

P

Pt

N

iPi

IIRts

Ic

}1,0{

04

1..

min

,::

1

i

jkjij

j

jkjij

j

i

P

PlPlPP

PlorPlPP

Pt

N

iPi

I

kiII

IRts

Ic

Ci is the cost of using path Pi