Presentation is loading. Please wait.

Presentation is loading. Please wait.

Expander Graphs: The Unbalanced Case Omer Reingold The Weizmann Institute.

Similar presentations

Presentation on theme: "Expander Graphs: The Unbalanced Case Omer Reingold The Weizmann Institute."— Presentation transcript:

1 Expander Graphs: The Unbalanced Case Omer Reingold The Weizmann Institute

2 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 ]

3 Bipartite 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

4 Vertex Expansion | (S)| A |S| (A > 1) S, |S| K Every (not too large) set expands. D NN

5 Vertex Expansion | (S)| A |S| (A > 1) S, |S| K Goal: minimize D (i.e. constant D) Degree 3 random graphs are expanders [Pin73] D NN

6 Vertex Expansion | (S)| A |S| (A > 1) S, |S| K Also: maximize A. Trivial upper bound: A D –even A D-1 Random graphs: A D-1 D NN

7 2 nd Eigenvalue Expansion D NN 2nd eigenvalue (in absolute value) of (normalized) adjacency matrix is bounded away from 1 Can be interpreted in terms of Renyi (l 2 ) entropy

8 Expanders 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 NN x x Induced dist. X

9 2 nd Eigenvalue Expansion XX D NN 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

10 2 nd Eigenvalue Expansion XX D NN Interestingly, vertex expansion and 2 nd - eigenvalue expansion are essentially equivalent for constant degree graphs [Tan84, AM84, Alo86]

11 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 i th neighbor of x (in time poly log N).

12 Explicit constructions – 2 nd Eigenvalue Celebrated sequence of algebraic constructions [ Mar73, GG80,JM85,LPS86,AGM87,Mar88,Mor94,... ]. Optimal 2 nd 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]

13 Explicit constructions – Vertex Expansion Optimal 2 nd 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

14 Property 1: A Very Strong Unique Neighbor Property S, |S| K, | (S)| 0.9 D |S| S Non 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

15 Incredibly Fault Tolerant Property 2: Incredibly Fault Tolerant S, |S| K, | (S)| 0.9 D |S| Remains a lossless expander even if adversary removes (0.7 D) edges from each vertex.

16 Explicit constructions – Vertex Expansion 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).

17 Unbalanced Expanders D NN Many applications need

18 Unbalanced Expanders D N M Many applications need unbalanced expanders:

19 Array of Definitions X X D N M Many flavors: –How unbalanced. –Measure of entropy. –Lossless vs. lossy. –Is X close to full entropy? –Lower vs. upper bound on entropy of X. –…–…

20 Vertex Expansion Revisited D N M Even previously trivial tasks require D = (log N/log M) M << N Farewell constant degree S, |S|= N 0.9 | (S)| 10 D

21 Slightly-Unbalanced Constant- Degree Lossless Expanders | (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/ )))

22 Open: More Unbalanced D N M E.g. M=N 0.5 and sets of size at most K=N 0.2 expand. While being greedy: Unique neighbor expanders Lossless expanders Minimal Degree

23 Super-Constant Degree D N M 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|

24 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 …

25 Increasing Entropy? Prob. dist. X Can Renyi entropy increase ? |Col(X)| min{M 0.5, N/M} D N M x x Induced dist. X

26 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 max T | Pr[X T] - Pr[Y T] | = ½ ||X-Y|| 1

27 Equivalently Extractors = Mixing D N M 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|= K T, | e(S,T)/DK - |T|/N | <

28 2-Source Extractors source of biased correlated bits almost uniform output E XT Recently – lots of attention and results Randomness Extractors are a special case, where the 2 nd source is truly random. another independent weak source random bits

29 Explicit Constructs. of Extractors 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=2 k(1- ) Selected open problem: M=2 k with D=Poly(LogN / ) Interpretation: extracting an arbitrary constant fraction of entropy Interpretation: extracting all the entropy

30 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

31 [ GUV06 ] - Basic Construction Left vertex f F q n (poly. of degree · n-1 over F q ) Edge Label y F Right vertices = F q m+1 y th neighbor of f = (y, f(y), (f h mod E)(y), (f h 2 mod E)(y), …, (f h m-1 mod E)(y)) where E(Y) = irreducible poly of degree n h = a parameter Thm: This is a (K,A) expander with K=h m, A = q-hnm.

32 Conclusions Many interesting variants of expander graphs Constructions in general – very far from optimal Any clean and useful algebraic characterization?

Download ppt "Expander Graphs: The Unbalanced Case Omer Reingold The Weizmann Institute."

Similar presentations

Ads by Google