finding a maximum independent set in a sparse random graph
Download
Skip this Video
Download Presentation
Finding a maximum independent set in a sparse random graph

Loading in 2 Seconds...

play fullscreen
1 / 20

Finding a maximum independent set in a sparse random graph - PowerPoint PPT Presentation


  • 120 Views
  • Uploaded on

Finding a maximum independent set in a sparse random graph. Uriel Feige and Eran Ofek. Max Independent Set. Largest set of vertices that induce no edge. NP-hard, even to approximate. NP-hard on planar graphs.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Finding a maximum independent set in a sparse random graph' - stian


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
max independent set
Max Independent Set

Largest set of vertices that induce no edge.

  • NP-hard, even to approximate.
  • NP-hard on planar graphs.
  • Polynomial time algorithms on “simple” graphs: trees (greedy), graphs of bounded treewidth (dynamic programming).
complexity on most graphs
Complexity on most graphs
  • On random graphs, no polynomial time algorithm is known to find max-IS.
  • Holds for all densities except for extremely sparse or extremely dense graphs.
  • “Best” algorithmic lower bound – greedy.
  • “Best” upper bound – theta function.
planted models
Planted models
  • Model the case that a graph happens to have an exceptionally large IS.
  • Random graph with edge probability d/n.
  • All edges within a random set S of vertices are removed.
  • When d|S| > n log n, the set S is likely to be the maximum IS.
some known results
Some known results
  • When d=n/2, can find S of size [Alon, Krivelevich, Sudakov 1998].
  • Can also certify maximality, and handle semirandom graphs [Feige, Krauthgamer 2000].
  • When d=log n, can find S of size , even in semirandom graphs, up to the point when it becomes NP-hard [Feige, Kilian 2001]
our results
Our results

Allow d to be (a sufficiently large) constant.

W.h.p., the random graph) has no independent set larger than n (log d)/d.

Plant S of size

We find max independent set in polynomial time.

New aspect: S is not the max-IS. Complicates analysis.

some related work
Some related work
  • Many of the techniques in this area were initiated in work of Alon and Kahale (1997) on coloring.
  • Amin Coja-Oghlan (2005): finds a planted bisection in a sparse random graph. The min bisection is not the planted one. Amin’s algorithm is based on spectral techniques and certifies minimality.
greedy algorithm
Greedy algorithm
  • Select vertex i to put in solution (e.g., vertex i may be vertex of degree 0, degree 1, or of lowest degree).
  • Remove neighbors of i.
  • Repeat on G – i – N(i).
simplify analysis
Simplify analysis

2-stage greedy

  • Select an independent set I.
  • Remove neighbors of I.
  • Finish off by exact algorithm.

Last stage takes polynomial time if G-I-N(I) has “simple” structure.

required properties of i
Required properties of I

Partition graph into Independent, Cover and Undecided.

  • No edge within I.
  • No edge between I and U.
  • Every vertex of C must have at least one neighbor in I.

Note: U is then precisely V(G) – I – N(I).

how we select i
How we select I

Initialization. Threshold t = d(1 - |S|/2n) < d.

  • Put vertices of degree lower than t in I.
  • Put vertices of degree higher than t in C.

Iteratively, move to U:

  • Vertices of I with neighbors in I or U.
  • Vertices of C with < 4 neighbors in I.
theorems for planted model
Theorems for planted model

Lem: S highly correlated with max-IS.

Lem: Low degree highly correlated with S.

Thm: I is contained in max-IS.

(Difficulty in proof: max-IS is not known not only to the algorithm, but also in analysis.)

Thm: G(U) has simple structure.

algorithm for g u
Algorithm for G(U)

Iteratively

  • Move vertices of degree 0 to I.
  • Move vertices of degree 1 to I, and their neighbors to C.

Use exhaustive search to find maximum IS in each of the remaining connected components.

Thm: CC of 2-core have size < O(log n).

why did we consider 2 core
Why did we consider 2-core?

Asymmetry: vertices of S enter U more easily than vertices of V-S.

A tree might have most its vertices from S.

In a cycle, at least half the vertices must be from V-S.

Easier to show that U has no large cycles then to show that has no large trees.

conclusions
Conclusions
  • Planted model in sparse graphs, in which planted solution is not optimal.
  • Natural algorithm provably finds max-IS in planted model. (All difficulties are hidden in the analysis.)
  • Improve tradeoff between d and |S|.
  • Output matching upper bound on |max-IS|.
ad