1 Bart Jansen Polynomial Kernels for Hard Problems on Disk Graphs Accepted for presentation at SWAT 2010

2 Overview Introduction Kernelization Graph classes Kernels Triangle Packing, K t -matching, H-matching Red/Blue Dominating Set Connected Vertex Cover Conclusion

3 Kernelization for graph problems Consider a computational decision problem on graphs Input: encoding x of a question about graph G and integer k. Question: does graph G have a (…)? Parameter:k Parameter expresses some property of the question (size of what we are looking for, treewidth of graph, etc.) A kernelization algorithm takes (x, k) as input and computes instance (x, k) of same problem in polynomial time, such that Answer to x is YES answer to x is YES k g(k) for some function g |x| f(k) for some function f The function f is the size of the kernel We want f to be a (small) polynomial

4 Recent kernelization results Bad news Many parameterized problems are W[1]-hard and have no kernels Several easier parameterized problems only have kernels where f is exponential Good news If we require G to be planar, lots of problems have linear or quadratic kernels Even if we relax planarity to bounded genus, H- minor-free, …

5 Expanding the range of good news The frameworks giving general good news about small kernels only apply under restrictions that make the graph G sparse: |E| c |V| Dense graphs without special structure make the problem hard, implying non-existence of kernels We consider graphs that exhibit structure, but are not sparse: (unit)disk graphs Yields good news: Red-Blue Dominating Set, H-Matching, Connected Vertex Cover Do not have polynomial kernels in general graphs Have polynomial kernels in (unit)disk graphs And the problems are still hard on disk graphs

6 Our kernels Quadratic edge count Subquadratic edge count Kernels for Dominating Set Linear edge count Meta-theorems Graph classes H-minor-free K i,j -subgraph-free bounded-genus planar unit-disk disk general

7 Disk graphs Consider a set S of closed disks in the plane The intersection graph of S: has a vertex v for every disk D(v), has an edge between u and v iff. the disks D(v) and D(u) intersect. (touching disks also intersect)

8 Properties of disk graphs If all disks have the same radius, their intersection graph is a unit disk graph All planar graphs are disk graphs (varying radii) Any clique is a (unit)disk graph Compare with K 5 which is not planar So there are disk graphs with Class of (unit)disk graphs Closed under vertex deletion Not closed under edge deletion Not closed under edge contraction

9 TRIANGLE PACKING AND H-MATCHING Structure theory and kernels

10 Triangle Packing Input:Graph G, integer k Question:Are there k vertex-disjoint triangles in G? Parameter:k NP-complete, even on planar graphs In FPT on general graphs with a O(k 2 )-vertex kernel

11 Triangle Packing Input:Graph G, integer k Question:Are there k vertex-disjoint triangles in G? Parameter:k Single reduction rule Try all O(n 3 ) sets of size 3, and test if they form a triangle Mark vertices that occur in a triangle Delete all vertices that were not marked

12 Kernelization algorithm Greedily build a maximal triangle packing Suppose the greedy packing contains k* copies If k* k The problem is solved Output a trivial YES instance If k* < k We prove: |V| is O(k*) is O(k)

13 Neighborhood Clique Lemma Let v be a vertex in a unit-disk graph G. Then there is a clique of size deg(v) / 6 among the neighbors of G. G[N(v)] has a clique of size deg(v) / 6 Proof. Consider centers of v and its neighbors in a disk realization Divide the plane into 6 equal sectors around v Some sector contains deg(v) / 6 sectors (Pigeonhole Principle) v v

14 v v Neighbors in each sector form a clique Assume every disk has radius ½ If v has a neighbor x then distance |xv| 1 v v x y

15 Neighbors in each sector form a clique Assume every disk has radius ½ If v has a neighbor x then distance |xv| 1 Consider two neighbors x,y in the same sector By adjacency to v: |xv| 1, |yv| 1 Sector definition:angle xvy 60 o By law of Cosines: |xy| 1 So x,y adjacent Neighbors within sector form a clique v v x y

16 Analysis of kernel size If there is a maximal triangle packing with k* copies in G, then |V| is O(k*) Proof. We divide V in two subsets: set S with vertices that are used in a selected copy set W with the remainder Since all triangles are vertex-disjoint, there are exactly 3k* vertices in S (every triangle uses 3 vertices) We bound the size of W 1.Every vertex in W must be adjacent to vertex in S 2.Every vertex in S has at most 12 neighbors in W So |W| 12 |S| 12(3 k*) O(k*)

17 Extension to K t -matching We get a kernel with O(k) vertices for Triangle Packing in unit-disk graphs Current best kernel for general graphs has O(k 2 ) vertices Generalizes to K t -matching for every fixed t Pack vertex-disjoint complete subgraphs of size t Important properties still hold: Every vertex that is not selected in a maximal packing must be adjacent to a selected vertex Every selected vertex has O(t) neighbors in W

18 Extension to H-Matching H-matching problem Pack vertex-disjoint copies of a fixed connected graph H Kernel with O(k |H|-1 ) vertices by H. Moser [SOFSEM 09] No kernel polynomial in |H| + k H-matching on unit-disk graphs H can be arbitrary Graph G in which we find the copies is a unit-disk graph Our result O(k)-vertex kernel for every fixed graph H Constant is exponential in the diameter of H Properties of maximal H-matching in reduced graph Every unused vertex has distance diameter(H) to a used vertex Every vertex has O(|H|) unused neighbors

19 RED/BLUE DOMINATING SET Structure theory and kernels

20 Red/Blue Dominating Set Input:Graph G with red vertices R, blue vertices B, integer k Question:Is there a set of k red vertices that dominate all blue vertices? Parameter:min(|R|,|B|)

21 Background min(|R|,|B|) as parameter since parameter k is W[1] hard, even on unit-disk graphs In FPT on general graphs, no polynomial kernel Usually assume G is bipartite with R and B as color classes We do not assume this here; bipartite disk graphs are planar Our results: O(min(|R|,|B|))-vertex kernel on planar graphs O(min(|R|,|B|) 2 )-vertex kernel on unit-disk graphs O(min(|R|,|B|) 4 )-vertex kernel on disk graphs

22 Reduction Rules 1.Red vertices r 1, r 2 such that N(r 1 ) B N(r 2 ) B Delete r 1 2.Blue vertices b1, b2 such that N(b 1 ) R N(b 2 ) R Delete b 2

23 Balance After exhaustive application of reduction rules, the color classes must be balanced Number of vertices in the classes must be polynomially related Easy for planar graphs: |R| 5|B| (and vice versa) Contribution: |R| O(|B| 2 ) (and vice versa) for unit-disk graphs |R| O(|B| 4 ) (and vice versa) for disk graphs These structural results immediately yield kernels

24 Balance in colored unit-disk graphs Usual model: two vertices adjacent iff their disks intersect Double the radius of disks Now: two vertices adjacent iff the disk of one contains the center of the other, and vice versa We prove: if no two red vertices see the same blue vertices, then |R| O(|B| 2 ). radius ½ radius 1

25 Proof We prove: if no two red vertices see the same blue vertices, then |R| O(|B| 2 ) Look at arrangement of the plane induced by blue circles Each region contains at most one red center Complexity of the arrangement is O(|B| 2 )

26 Reconsider usual model: vertices adjacent iff disks intersect We prove: if no red disk sees a subset of the blue vertices seen by another red disk, then |R| O(|B| 4 ) Balance in colored disk graphs A B C [A,B][B,A][B,A,C][A,B,C]

27 [B,A,C][A,B,C] Balance in colored disk graphs [B,C,A] A B C [C,B,A][C,A,B] [A,C,B] A face in the arrangement of bisector curves determines a unique order of encountering blue disks The blue neighbors of a red disk are a prefix of the string determined by the face containing its center So any face contains at most one red disk

28 [B,A,C][A,B,C] Balance in colored disk graphs [B,C,A] [C,B,A][C,A,B] [A,C,B] Given n curves for which each pair intersects O(1) times, the complexity of the arrangement is O(n 2 ) We have O(|B| 2 ) curves, hence complexity is O(|B| 4 ) Total number of red disks is O(|B| 4 )

29 Summary of kernels for Red/Blue Dominating Set By applying the reduction rules we find in polynomial time an equivalent instance such that no red vertex sees a subset of what another red vertex sees Same for the blue vertices Structural theorems show that in such colored graphs the sizes of the color classes are polynomially related So size of the largest class is polynomial in the size of smallest class Hence |V| = |R| + |B| min(|R|+|B|) + max(|R|,|B|) is O(min(|R|+|B|) c )

30 CONNECTED VERTEX COVER Structure theory and kernels

31 Connected Vertex Cover Input:Graph G, integer k Question:Is there a vertex cover of k vertices that induces a connected subgraph? Parameter:k FPT on general graphs, no polynomial kernel Trivial linear-vertex kernel on unit-disk graphs Any vertex cover for a unit-disk graph must have size n/12 (Erik-Jans thesis)

32 Annotated Connected Vertex Cover Input:Graph G, set of marked vertices S, integer k Question:Is there a vertex cover of k vertices that induces a connected subgraph, and which contains all marked vertices? Parameter:k Unmarked vertex v is dead if all its neighbors are marked, if not then v is live Reduction rules 1. Unmarked vertex v with degree > k: mark v 2. Distinct dead vertices u,v such that N(u) N(v): delete u

33 Analysis Call an edge covered if its incident on a marked vertices Otherwise an edge is uncovered > k 2 uncovered edges: output NO > k marked vertices: output NO In remaining cases k 2 uncovered edges 2k 2 live vertices since each live vertex is incident on an uncovered edge k marked vertices Remains to bound the dead vertices # Dead vertices can be bounded in # marked vertices by the balance argument, gives #dead is O(k 4 ) More intricate argument gives O(k 2 ) bound Annotation can be undone

34 Conclusion and discussion Several parameterized problems without polynomial kernels on general graphs, do allow polynomial kernels on dense (unit)disk graphs Colored K i,j -subgraph-free graphs also have the polynomial balance property Polynomial kernels for Red/Blue Dom. Set and Connected V.C. Open problems Poly kernel for H-matching in disk graphs? Poly kernel for unit-disk Edge Clique Cover? Poly kernel for unit-disk Partition (Vertex Set) Into Cliques? Improve the quartic bound for balance in disk graphs Find other problems where colored graph balance implies poly kernels PlanarUnit-diskDisk H-matchingO(k) [Known]O(k)? Red/Blue Dominating SetO(min(|R|,|B|))O(min(|R|,|B|) 2 )O(min(|R|,|B|) 4 ) Connected Vertex Cover14k [Known]12k3k 2 + 7k