Expander Graphs: The Unbalanced Case Omer Reingold The Weizmann Institute

Expander Graphs: The Unbalanced Case

Omer ReingoldOmer Reingold

The Weizmann The Weizmann InstituteInstitute

What's in This Talk?What's in This Talk?

• Expander Graphs – an array of definitions.• Focus on most established notions, and

open problems on explicit constructions. Mainly in the unbalanced case since this is– What applications often require– Where constructions are very far from

optimal• Will flash one construction (no details) -

Unbalanced expanders based on Parvaresh-Vardy Codes [Guruswami,Umans,Vadhan 06]

Bipartite GraphsBipartite Graphs•As a preparation for the unbalanced case we will talk of bipartite expanders.

•Can also capture undirected expanders:

D

N

Symmetric

N

D

G - Undirected

N

Vertex ExpansionVertex Expansion

|(S)| A |S|

(A > 1)

S, |S| K

Every (not too large) set expands.

D

N N

Vertex ExpansionVertex Expansion

|(S)| A |S|

(A > 1)

S, |S| K

•Goal: minimize D (i.e. constant D) •Degree 3 random graphs are

expanders [Pin73]

D

N N

Vertex ExpansionVertex Expansion

|(S)| A |S|

(A > 1)

S, |S| K

Also: maximize A.• Trivial upper bound: A D

– even A ≲ D-1• Random graphs: AD-1

D

N N

22ndnd Eigenvalue Expansion Eigenvalue Expansion

D

N N

• 2nd eigenvalue (in absolute value) of (normalized) adjacency matrix is bounded away from 1

• Can be interpreted in terms of Renyi (l2) entropy

Expanders Add EntropyExpanders Add Entropy

Prob. dist. X

•Vertex expansion: |Support(X’)| A |Support(X)|

•Some applications rely on “less naïve” measures of entropy.

•Col(X) = Pr[X(1)=X(2)] = ||X||2

D

N N

x x’

Induced dist. X’

22ndnd Eigenvalue Expansion Eigenvalue Expansion

X’X D

N N

• Col(X’) –1/N 2 (Col(X) –1/N)• Renyi entropy (log 1/Col(X)) increases as long as:

< 1 and Col(X) is not too small

22ndnd Eigenvalue Expansion Eigenvalue Expansion

X’X D

N N

• Interestingly, vertex expansion and 2nd-eigenvalue expansion are essentially equivalent for constant degree graphs [Tan84, AM84, Alo86]

Explicit Constructions

Applications need explicit constructions:• Weakly explicit: easy to build the entire graph (in time poly N).• Strongly explicit:

– Given vertex name x and edge label i easy to find the ith neighbor of x (in time poly log N).

Explicit constructions – 2Explicit constructions – 2ndnd EigenvalueEigenvalue

• Celebrated sequence of algebraic constructions [Mar73, GG80,JM85,LPS86,AGM87,Mar88,Mor94,...].

• Optimal 2nd eigenvalue (Ramanujan graphs)

• “Combinatorial” constructions: [Ajt87, RVW00, BL04].

• Open: Combinatorial constructions of strongly explicit Ramanujan (or almost Ramanujan) graphs.

• Getting “close”: [Ben-Aroya,Ta-Shma 08]

Explicit constructions – Vertex Explicit constructions – Vertex ExpansionExpansion

• Optimal 2nd eigenvalue expansion does not imply optimal vertex expansion

• Exist Ramanujan graphs with vertex expansion D/2 [Kah95].

• Lossless Expander – Expansion > (1-) D

• Why should we care?– Limitation of previous techniques– Many applications

Property 1: A Very Strong Unique Neighbor Property

S, |S| K, |(S)| 0.9 D |S|

SNon Unique neighbor

S has 0.8 D |S| unique neighbors !

• We call graphs where every such S has even a single unique neighbor – unique neighbor expanders

Unique neighbor of S

Property 2: Incredibly Incredibly Fault TolerantFault Tolerant

S, |S| K, |(S)| 0.9 D |S|

Remains a lossless expander even if adversary removes (0.7 D) edges from each vertex.

Explicit constructions – Vertex Explicit constructions – Vertex ExpansionExpansion

• Open: lossless expanders for the undirected case.– Unique neighbor expanders are known

[AC02]

• For the directed case (expansion only from left side), lossless expanders are known [CRVW02]. Expansion D-O(D).

• Open: expansion D-O(1) (even with non-constant degree).

Unbalanced Expanders

D

N N

• Many applications need

Unbalanced Expanders

D

NM

• Many applications need unbalanced expanders:

Array of Definitions

X’X D

NM

• Many flavors:– How unbalanced. – Measure of entropy.– Lossless vs. lossy.– Is X’ close to full entropy?– Lower vs. upper bound on entropy of X.– …

Vertex Expansion Revisited

D

NM

• Even previously trivial tasks require D = (log N/log M)

• M << N Farewell constant degree

S, |S|= N 0.9|(S)| 10 D

Slightly-Unbalanced Slightly-Unbalanced Constant-Degree Lossless Constant-Degree Lossless

ExpandersExpanders

|(S)| (1-) D |S|

D

N M= N

CRVW02:

0<, 1 constants D constant & K= (N)

S, |S| K

In case someone asks: K= ( M/D) & D= poly(1/ , log (1/ )) (fully explicit: D= quasipoly(1/ , log (1/ )))

Open: More Unbalanced

D

NM

• E.g. M=N0.5 and sets of size at most K=N0.2 expand. While being greedy:• Unique neighbor expanders• Lossless expanders• Minimal Degree

Super-Constant Degree

D

NM

• State of the art [GUV06]: D=Poly(LogN), M=Poly(KD) (w. some tradeoff).

• Open: M=O(KD) (known w. D=QuasiPoly(LogN))

• Open: D= O(LogN)

S, |S| K |(S)| (1-)D |S|

Dispersers [Sipser 88]Dispersers [Sipser 88]N M

D |(S)| >

(1-) M

S, |S|≥ K

• Bounds: •D ≥ 1/ log(N/K)•DK/M ≥ log 1/ -- must be lossy

• Explicit constructions are (comparably) good but still not optimal …

Increasing Entropy?Increasing Entropy?

Prob. dist. X

•Can Renyi entropy increase ?

• |Col(X’)| < |Col(X)| (essentially) D> min{M0.5, N/M}

D

NM

x x’

Induced dist. X’

Extractors [NZ 93]

X’X D

N M ≪ N

• (k,)-extractor if Min-entropy(X) k X’ -close to uniform

• Min-entropy(X) k if x, Pr[x] 2-k

• X and Y are -close if maxT | Pr[XT] - Pr[YT] | = ½ ||X-Y||1

Equivalently Extractors = Mixing

D

NM

• Vertex Expansion – Sets on the left have many neighbors.

• Mixing Lemma – the neighborhood of S hits any T with roughly the right proportion.

S, |S|= KT,

| e(S,T)/DK - |T|/N | <

2-Source Extractors2-Source Extractors

source of biased correlated bits almost uniform outputEXT

• Recently – lots of attention and results• Randomness Extractors are a special

case, where the 2nd source is truly random.

another independent weak source

random bits

Explicit Constructs. of Explicit Constructs. of ExtractorsExtractors

• Extractors are highly motivated in applications. As a general rule of thumb: “Anything expanders can do, extractors can do better” …

• Lots of progress. Still very far from optimal. Best in one direction [LRVW03, GUV06]: D=Poly(LogN / ), M=2k(1-)

• Selected open problem: M=2k with D=Poly(LogN / )

Interpretation: extracting an arbitrary constant fraction of entropy

Interpretation: extracting all the entropy

A Word About Techniques

• Research on randomness extractors was invigorated with the discovery of a beautiful and surprising connection to pseudorandom generators [Tre99].

• This further led to discoveries of connections between extractors and error correcting codes [Tre99, RRV99, TZ01, TZS01, SU01].

• In particular, [GUV06] relies on Parvaresh-Vardy list-decodable codes

[GUV06] - Basic Construction• Left vertex f Fq

n (poly. of degree· n-1 over Fq)

• Edge Label y F

• Right vertices = Fqm+1

y’th neighbor of f =

(y, f(y), (f h mod E)(y), (f h2 mod E)(y), …, (f hm-1 mod E)(y))

where E(Y) = irreducible poly of degree n h = a parameter

Thm: This is a (K,A) expander with K=hm, A = q-hnm.

Conclusions

• Many interesting variants of expander graphs

• Constructions in general – very far from optimal

• Any clean and useful algebraic characterization?