Clique-Width of Monogenic Bipartite Graphs Jordan Volz DIMACS REU 2006 Mentor: Dr. Vadim Lozin, RUTCOR.

Slides:



Advertisements
Similar presentations
NP-Hard Nattee Niparnan.
Advertisements

Connectivity - Menger’s Theorem Graphs & Algorithms Lecture 3.
1 Decomposing Hypergraphs with Hypertrees Raphael Yuster University of Haifa - Oranim.
Edge-connectivity and super edge-connectivity of P 2 -path graphs Camino Balbuena, Daniela Ferrero Discrete Mathematics 269 (2003) 13 – 20.
Polynomial-time reductions We have seen several reductions:
Approximating Maximum Subgraphs Without Short Cycles Guy Kortsarz Join work with Michael Langberg and Zeev Nutov.
1 NP-completeness Lecture 2: Jan P The class of problems that can be solved in polynomial time. e.g. gcd, shortest path, prime, etc. There are many.
B IPARTITE I NDEX C ODING Arash Saber Tehrani Alexandros G. Dimakis Michael J. Neely Department of Electrical Engineering University of Southern California.
The Theory of NP-Completeness
GOLOMB RULERS AND GRACEFUL GRAPHS
Train DEPOT PROBLEM USING PERMUTATION GRAPHS
Between 2- and 3-colorability Rutgers University.
Complexity 16-1 Complexity Andrei Bulatov Non-Approximability.
Complexity 11-1 Complexity Andrei Bulatov NP-Completeness.
Mycielski’s Construction Mycielski’s Construction: From a simple graph G, Mycielski’s Construction produces a simple graph G’ containing G. Beginning with.
Balanced Graph Partitioning Konstantin Andreev Harald Räcke.
Computability and Complexity 15-1 Computability and Complexity Andrei Bulatov NP-Completeness.
Techniques for Proving NP-Completeness
CPSC 689: Discrete Algorithms for Mobile and Wireless Systems Spring 2009 Prof. Jennifer Welch.
Matchings Matching: A matching in a graph G is a set of non-loop edges with no shared endpoints Maximal Matching: A maximal matching in a graph is a matching.
1 CSE 417: Algorithms and Computational Complexity Winter 2001 Lecture 23 Instructor: Paul Beame.
A New Algorithm for Optimal 2-Constraint Satisfaction and Its Implications Ryan Williams Computer Science Department, Carnegie Mellon University Presented.
Job Scheduling Lecture 19: March 19. Job Scheduling: Unrelated Multiple Machines There are n jobs, each job has: a processing time p(i,j) (the time to.
Solving the Maximum Independent Set Problem for -free planar graphs Sarah Bleiler DIMACS REU 2005 Advisor: Dr. Vadim Lozin, RUTCOR.
Clique Width of Monogenic Bipartite Graphs Jordan Volz DIMACS REU 2006 Mentor: Dr. Vadim Lozin, RUTCOR.
Clique Cover Cook’s Theorem 3SAT and Independent Set
Graph Theory Ch.5. Coloring of Graphs 1 Chapter 5 Coloring of Graphs.
Chapter 5: Computational Complexity of Area Minimization in Multi-Layer Channel Routing and an Efficient Algorithm Presented by Md. Raqibul Hasan Std No.
K-Coloring k-coloring: A k-coloring of a graph G is a labeling f: V(G)  S, where |S|=k. The labels are colors; the vertices of one color form a color.
The Maximum Independent Set Problem Sarah Bleiler DIMACS REU 2005 Advisor: Dr. Vadim Lozin, RUTCOR.
K-Coloring k-coloring: A k-coloring of a graph G is a labeling f: V(G)  S, where |S|=k. The labels are colors; the vertices of one color form a color.
1 The Theory of NP-Completeness 2 NP P NPC NP: Non-deterministic Polynomial P: Polynomial NPC: Non-deterministic Polynomial Complete P=NP? X = P.
The Theory of NP-Completeness 1. Nondeterministic algorithms A nondeterminstic algorithm consists of phase 1: guessing phase 2: checking If the checking.
1 The Theory of NP-Completeness 2012/11/6 P: the class of problems which can be solved by a deterministic polynomial algorithm. NP : the class of decision.
Nattee Niparnan. Easy & Hard Problem What is “difficulty” of problem? Difficult for computer scientist to derive algorithm for the problem? Difficult.
1 Treewidth, partial k-tree and chordal graphs Delpensum INF 334 Institutt fo informatikk Pinar Heggernes Speaker:
The Turán number of sparse spanning graphs Raphael Yuster joint work with Noga Alon Banff 2012.
Lecture 22 More NPC problems
1 Edge-bipancyclicity of star graphs under edge-fault tolerant Applied Mathematics and Computation, Volume 183, Issue 2, 15 December 2006, Pages
Approximating the Minimum Degree Spanning Tree to within One from the Optimal Degree R 陳建霖 R 宋彥朋 B 楊鈞羽 R 郭慶徵 R
CSE, IIT KGP Graph Theory: Introduction Pallab Dasgupta Dept. of CSE, IIT
Fan-planar Graphs: Combinatorial Properties and Complexity results Carla Binucci, Emilio Di Giacomo, Walter Didimo, Fabrizio Montecchiani, Maurizio Patrignani,
NP-complete Problems SAT 3SAT Independent Set Hamiltonian Cycle
Polynomial-time reductions We have seen several reductions:
NP-COMPLETENESS PRESENTED BY TUSHAR KUMAR J. RITESH BAGGA.
Techniques for Proving NP-Completeness Show that a special case of the problem you are interested in is NP- complete. For example: The problem of finding.
Indian Institute of Technology Kharagpur PALLAB DASGUPTA Graph Theory: Introduction Pallab Dasgupta, Professor, Dept. of Computer Sc. and Engineering,
Mycielski’s Construction Mycielski’s Construction: From a simple graph G, Mycielski’s Construction produces a simple graph G’ containing G. Beginning with.
Data Structures & Algorithms Graphs
Chapter 1 Fundamental Concepts Introduction to Graph Theory Douglas B. West July 11, 2002.
NP-COMPLETE PROBLEMS. Admin  Two more assignments…  No office hours on tomorrow.
The Dominating Set and its Parametric Dual  the Dominated Set  Lan Lin prepared for theory group meeting on June 11, 2003.
The Vertex Arboricity of Integer Distance Graph with a Special Distance Set Juan Liu* and Qinglin Yu Center for Combinatorics, LPMC Nankai University,
Connectivity and Paths 報告人:林清池. Connectivity A separating set of a graph G is a set such that G-S has more than one component. The connectivity of G,
1 Decomposition into bipartite graphs with minimum degree 1. Raphael Yuster.
Locally constrained graph homomorphisms Jan Kratochvíl Jan Kratochvíl Charles University, Prague.
The Evolution of a Hard Graph Theory Problem – Secure Sets Ron Dutton Computer Science University of Central Florida 1.
NP-Completeness (Nondeterministic Polynomial Completeness) Sushanth Sivaram Vallath & Z. Joseph.
Fixed parameter algorithms for protein similarity search under mRNA structure constrains A joint work by: G. Blin, G. Fertin, D. Hermelin, and S. Vialette.
CS 461 – Nov. 30 Section 7.5 How to show a problem is NP-complete –Show it’s in NP. –Show that it corresponds to another problem already known to be NP-complete.
1 Latency-Bounded Minimum Influential Node Selection in Social Networks Incheol Shin
Introduction to Graph Theory
NP-completeness NP-complete problems. Homework Vertex Cover Instance. A graph G and an integer k. Question. Is there a vertex cover of cardinality k?
1 The Theory of NP-Completeness 2 Review: Finding lower bound by problem transformation Problem X reduces to problem Y (X  Y ) iff X can be solved by.
The NP class. NP-completeness Lecture2. The NP-class The NP class is a class that contains all the problems that can be decided by a Non-Deterministic.
Graph Coloring.
Algorithms for Finding Distance-Edge-Colorings of Graphs
Computability and Complexity
REDUCESEARCH Polynomial Kernels for Hitting Forbidden Minors under Structural Parameterizations Bart M. P. Jansen Astrid Pieterse ESA 2018 August.
Minimal Universal Bipartite Graphs
Presentation transcript:

Clique-Width of Monogenic Bipartite Graphs Jordan Volz DIMACS REU 2006 Mentor: Dr. Vadim Lozin, RUTCOR

Overview Provide definitions Present problem Provide necessary tools Outline Solution

Definitions Graph: Vertices and Edges Bipartite Graph: A graph whose vertices can be partition into two sets W and B where vertices of one set are adjacent to only vertices of the other set. Induced Subgraph: If A is a subset of V(G), then the graph formed by the vertices of A and the edges between them is an induced subgraph of G. Forbidden Induced Subgraph: A graph H that cannot appear as an induced subgraph in G Monogenic Class: A class of graphs defined by a single forbidden induced subgraph H.

Clique Width: The minimum number of labels needed to construct a graph G using the following 4 operations : i(v): Creation of a new vertex v with label I G + H: Disjoint union of two labeled graphs  i,j : Join all vertices of label i to label j  i  j : re-label all vertices of label i to label j We say a family of graphs F has bounded clique- width if there exists an integer k such that for any graph G in F, G can be constructed using k labels. If no such k exists, F has unbounded clique- width. Clique-Width

  Clique-Width Example t 1 =  r  b [  b,r (  w,b (w(1)+b(2))+  w,r (w(5) + r(6)))] t 2 =  b  r [  b,r (  w,b (w(4)+b(3))+  w,r (w(8) + r(7)))]  b,r (t 2 + t 2 ) Clique Width is at most 3.     

Why Study Clique Width? In general, there is no known method to solve NP-hard problems in polynomial time. Many NP-hard problems have polynomial-time solutions restricted to graphs of bounded clique width (Courcelle, Engelfriet, Rozenberg, 1993). Bipartite graphs have unbounded clique width in the general case (grids, permutation graphs). We’re interested in monogenic classes of bipartite graphs, specifically K 1,3 -free and 2P 3 -free graphs.

Forbidden Induced Subgraphs Lozin and Rautenbach described 8 graphs in S that are self- complementary. Previously results were known for A 1 and A 2 (bounded), as well as A 3 and A 5 together. We will resolve the case for A 3, A 5, A 6, and A 8.        

Tools for Proof of K 1,3 +e K+S Graph: a bipartite graph that can be partitioned into a biclique and an independent set. Well Orderable Graph: A bipartite graph whose vertex set can be ordered x 1,…x n such that: N {x 2,…x n } (x 1 )={x 2 } or N {x 2,…x n } (x 1 )= {x 4,…x n }. For 1<i<n, if N {x i,…x n } (x i-1 )={x i } then N {x i+1,…x n } (x i )= {x i+3,…x n } and if N {x i,…x n } (x i-1 )= {x i+3,…x n } then N {x i+1,…x n } (x i )= {x i+1 }. Clique-Width of a well orderable graph is at most 5. K S

Outline of Proof for K 1,3 +e We know that the class of S 1,2,3 -free have bounded clique width (Lozin 2002). But, S 1,2,3 is H 7 ! Thus we conclude that a K 1,3 +e – free graph must have an induced subgraph that is a well-orderable graph of size at least 7. When G has a well orderable subgraph H p, we then can break the problem down into 3 cases, when p>10, when p=8 or 9, and when p=7. In all three cases we use similar logic to show that G must have bounded clique width, using lots of

Tools for Proof of 2P 3 Bipartite Chain Graph: A bipartite graph with vertex ordering b 1,…,b n and w 1,…,w, m such that N(b i ) is contained in N(b i+1 ) (N(w i ) is contained in N(w i-1 )). Bipartite chain graphs have bounded clique width, and even multi-layered bipartite chain graphs have bounded clique width. But, 2P 3 -free graphs have unbounded clique-width!

Unbounded clique-width How do we show a graph has unbounded clique-width? To show bounded clique width, you need only to produce an algorithm to construct a k-expression. There are very few proofs for showing graphs have unbounded clique width. So we steal an existing proof and modify it to our needs. What we need is one example of a 2P 3 free graph that has unbounded clique-width, and then that characterizes the entire class.

Outline of Proof for 2P 3 Brandstadt et al (2003) proved that (K 4, 2K 2 )-free graphs have unbouded clique-width by using the following type of graph: We observe that the following graph is 2P 3 free:

Outline of Proof for 2P 3 Then we find a set of n vertices that we split into three different groups based on their adjacencies in the graph. Each set we prove has pairwise different labels, so we conclude that this graph uses at least n/3 labels. Since A 6 and A 8 contain A 3, we also conclude that this exact same graph works to show their respective graph classes have unbounded clique width as well.

Open Questions Prove unbounded clique width of A 4 -free graphs (and hence A 7 ) Prove bounded clique width of A 7 -free graphs (and hence A 4 ) Prove bounded clique width of A 4 -free graphs and unbounded clique width of A 4 -free graphs. More info: dimax.rutgers.edu/~jordanv