On the complexity of H-coloring
Journal of Combinatorial Theory Series B
The Complexity of Multiterminal Cuts
SIAM Journal on Computing
Algorithmic theory of random graphs
Random Structures & Algorithms - Special issue: average-case analysis of algorithms
List homomorphisms to reflexive graphs
Journal of Combinatorial Theory Series B
Polynomial time approximation schemes for dense instances of NP -hard problems
Journal of Computer and System Sciences
An improved approximation algorithm for MULTIWAY CUT
Journal of Computer and System Sciences - 30th annual ACM symposium on theory of computing
Information Processing Letters
On Markov chains for randomly H-coloring a graph
Journal of Algorithms
Relations between average case complexity and approximation complexity
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
The RPR2 Rounding Technique for Semidefinite Programs
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
On the Approximability of the Maximum Common Subgraph Problem
STACS '92 Proceedings of the 9th Annual Symposium on Theoretical Aspects of Computer Science
The Complexity of Restrictive H-Coloring
WG '02 Revised Papers from the 28th International Workshop on Graph-Theoretic Concepts in Computer Science
More on Average Case vs Approximation Complexity
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Rounding Algorithms for a Geometric Embedding of Minimum Multiway Cut
Mathematics of Operations Research
Maximizing Quadratic Programs: Extending Grothendieck's Inequality
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Corrigendum: the complexity of counting graph homomorphisms
Random Structures & Algorithms
Combination can be hard: approximability of the unique coverage problem
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
A maximal tractable class of soft constraints
Journal of Artificial Intelligence Research
Communication: Level of repair analysis and minimum cost homomorphisms of graphs
Discrete Applied Mathematics
Approximability Distance in the Space of H-Colourability Problems
CSR '09 Proceedings of the Fourth International Computer Science Symposium in Russia on Computer Science - Theory and Applications
Optimal allocation in combinatorial auctions with quadratic utility functions
TAMC'11 Proceedings of the 8th annual conference on Theory and applications of models of computation
Combinatorial auctions with restricted complements
Proceedings of the 13th ACM Conference on Electronic Commerce
MFCS'12 Proceedings of the 37th international conference on Mathematical Foundations of Computer Science
Hi-index | 0.00 |
We introduce the maximum graph homomorphism (MGH) problem: given a graph G, and a target graph H, find a mapping ϕ:VG↦VH that maximizes the number of edges of G that are mapped to edges of H. This problem encodes various fundamental NP-hard problems including Maxcut and Max-k-cut. We also consider the multiway uncut problem. We are given a graph G and a set of terminals T⊆VG. We want to partition VG into |T| parts, each containing exactly one terminal, so as to maximize the number of edges in EG having both endpoints in the same part. Multiway uncut can be viewed as a special case of prelabeled MGH where one is also given a prelabeling ${\ensuremath{\varphi}}':U\mapsto V_H,\ U{\subseteq} V_G$, and the output has to be an extension of ϕ′. Both MGH and multiway uncut have a trivial 0.5-approximation algorithm. We present a 0.8535-approximation algorithm for multiway uncut based on a natural linear programming relaxation. This relaxation has an integrality gap of $\frac{6}{7}\simeq 0.8571$, showing that our guarantee is almost tight. For maximum graph homomorphism, we show that a $\bigl({\ensuremath{\frac{1}{2}}}+{\ensuremath{\varepsilon}}_0)$-approximation algorithm, for any constant ε00, implies an algorithm for distinguishing between certain average-case instances of the subgraph isomorphism problem that appear to be hard. Complementing this, we give a $\bigl({\ensuremath{\frac{1}{2}}}+\Omega(\frac{1}{|H|\log{|H|}})\bigr)$-approximation algorithm.