Finding a maximum independent set in a sparse random graph
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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.

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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)


  • 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.


  • 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|.