Non-Hierarchical Sequencing Graphs. Algorithmic Graph Theory2.
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Transcript of Non-Hierarchical Sequencing Graphs. Algorithmic Graph Theory2.
Non-Hierarchical Sequencing Graphs
Algorithmic Graph Theory 2
example
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Example
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Algorithmic Graph Theory and its Applications
Martin Charles Golumbic
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Algorithmic Graph Theory and its Applications
Martin Charles Golumbic
Algorithmic Graph Theory 34
Algorithmic Graph Theory and its Applications
Martin Charles Golumbic
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Introduction
Intersection Graphs Interval Graphs Greedy Coloring The Berge Mystery Story Other Structure Families of Graphs Graph Sandwich Problems Probe Graphs and Tolerance Graphs
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Theconcept of an intersection graph applications in computation operations research molecular biology scheduling designing circuits rich mathematical problems
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Defining some terms
graph: a collection of vertices and edges coloring a graph:
assigning a color to every vertex, such that
adjacent vertices have different colors
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independent set: a collection of vertices
NO two of which are connected
Example: { d, e, f } or the green set
clique (or complete set):
EVERY two of which are connected
Example: { a, b, d } or { c, e }
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complement of a graph:
interchanging the edges and the non-edges
The complement G The original graph G__
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directed graph: edges have directions
(possibly both directions)
orientation: exactly ONE direction per edge
cyclic orientation acyclic orientation
Interval GraphsThe intersection graphs of intervals on a line:
- create a vertex for each interval
- connect vertices when their intervals intersect
Jan Feb Mar Apr May Jun July Sep Oct Nov Dec
Phase 1Phase 1
Phase 2Phase 2
Phase 3Phase 3Task 4
Task 5
1 2 3
4 5The interval graph G
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History of Interval Graphs Hajos 1957: Combinatorics (scheduling) Benzer 1959: Biology (genetics) Gilmore & Hoffman 1964: Characterization Booth & Lueker 1976: First linear time
recognition algorithm
Many other applications:mobile radio frequency assignmentVLSI designtemporal reasoning in AIcomputer storage allocation
Scheduling Example
Lectures need to be assigned classrooms at the University. Lecture #a: 9:00-10:15 Lecture #b: 10:00-12:00 etc.
Conflicting lectures Different rooms How many rooms?
Scheduling Example (cont.)
Scheduling Example (graphs)
(a) The interval graph (b) Its complement (disjointness)
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Coloring Interval Graphs interval graphs have special properties used to color them efficiently coloring algorithm sweeps across from
left to right assigning colors in a ``greedy manner” This is optimal !
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Coloring Interval Graphs
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Coloring Intervals (greedy)
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Is greedy the best we can do? Can we prove optimality? Yes: It uses the smallest # colors.
Proof: Let k be the number of colors used.
Look at the point P, when color k was used first.
At P all the colors 1 to k-1 were busy!
We are forced to use k colors at P.
And, they form a clique of size k in the interval graph.
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Coloring Intervals (greedy)P (needs 4 colors)
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Coloring Interval Graphs
The clique at point P
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Greedy the best we can do !
Formally,
(1) at least k colors are required
(because of the clique)
(2) greedy succeeded using k colors.
Therefore,
the solution is optimal. Q.E.D.
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Characterizing Interval Graphs Properties of interval graphs How to recognize them Their mathematical structure
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Characterizing Interval Graphs Properties of interval graphs How to recognize them Their mathematical structure
Two properties characterize interval graphs:
- The Chordal Graph Property
- The co-TRO Property
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The Chordal Graph Property
chordal graph:
every cycle of length > 4 has a chord
(connecting two vertices that are not consecutive)
i.e., they may not contain chordless cycles!
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Interval Graphs are Chordal
Interval graphs may not contain chordless cycles!
- i.e., they are chordal. Why?
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Interval Graphs are Chordal
Interval graphs may not contain chordless cycles!
- i.e., they are chordal. Why?
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The co-TRO Property
The transitive orientation (TRO) of the complement
i.e., the complement must have a TRO
Not transitive ! Transitive !
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Interval Graphs are co-TRO
The complement of an Interval graph has a transitive orientation!
- Why?
The complement is the disjointness graph.
So, orient from the earlier interval
to the later interval.
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Gilmore and Hoffman (1964)
Theorem:
A graph G is an interval graph
if and only if G Is chordal and
its complement G is transitively orientable. __
This provides the basis for the first set of recognition algorithms in the early 1970’s.
A Mystery in the LibraryThe Berge Mystery Story:The Berge Mystery Story:
Six professors had been to the library on the day that the rare tractate was stolen.
Each had entered once, stayed for some time and then left.
If two were in the library at the same time, then at least one of them saw the other.
Detectives questioned the professors and gathered the following testimony:
Abe said that he saw Burt and Eddie Burt reported that he saw Abe and Ida Charlotte claimed to have seen
Desmond and Ida Desmond said that he saw Abe and Ida Eddie testified to seeing Burt and Charlotte Ida said that she saw Charlotte and Eddie
One of the Professor LIED !! Who was it?One of the Professor LIED !! Who was it?
The Facts:The Facts:
Solving the Mystery
The Testimony Graph
Clue #1:
Double arrows imply TRUTH
Solving the Mystery
Undirected Testimony Graph
We know there is a lie, since {A, B, I, D} is a chordless 4-cycle.
cycle
Intersecting Intervals cannot form Chordless Cycles
Burt Desmond
Abe
No place for Ida’s interval: It must hit both B and D but cannot hit A.
Impossible!
Solving the Mystery
There are three chordless 4-cycles:{A, B, I, D} {A, D, I, E} {A, E, C, D}
Burt is NOT a liar: He is missing from the second cycle. Ida is NOT a liar: She is missing from the third cycle. Charlotte is NOT a liar: She is missing from the second. Eddie is NOT a liar: He is missing from the first cycle.
WHO IS THE LIAR? Abe or Desmond ?
One professor from the chordless 4-cycle must be a liar.One professor from the chordless 4-cycle must be a liar.
Solving the Mystery (cont.)
WHO IS THE LIAR? Abe or Desmond ?
If Abe were the liar and Desmond truthful, then {A, B, I, D} would remain a chordless 4-cycle, since B and I are truthful.
Therefore:
Desmond is the liar.
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Was Desmond Stupid or Just Ignorant?
If Desmond had studied algorithmic graph theory, he would have known that his testimony to the police would not hold up.
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Many other Families of Intersection Graphs
Victor Klee, in a paper in 1969:
``What are the intersection graphs of arcs in a circle?’’
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Many other Families of Intersection Graphs
Victor Klee, in a paper in 1969:
``What are the intersection graphs of arcs in a circle?“
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Many other Families of Intersection Graphs
Victor Klee, in a paper in 1969:
``What are the intersection graphs of arcs in a circle?“
Klee’s paper was an implicit challenge
- consider a whole variety of problems
- on many kinds of intersection graphs.
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Families of Intersection Graphs boxes in the plane paths in a tree chords of a circle spheres in 3-space trapezoids, parallelograms, curves of functions many other geometrical and topological bodies
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Families of Intersection Graphs boxes in the plane paths in a tree chords of a circle spheres in 3-space trapezoids, parallelograms, curves of functions many other geometrical and topological bodies
The Algorithmic Problems:– recognize them– color them– find maximum cliques – find maximum independent sets
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A small hierarchy
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Bell Labs in New Jersey (Spring 1981)
John Klincewicz: Suppose you are routing phone calls in a tree network. Two calls interfere if they share an edge of the tree. How can you optimally schedule the calls?
The Story Begins
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Bell Labs in New Jersey (Spring 1981)
John Klincewicz: Suppose you are routing phone calls in a tree network. Two calls interfere if they share an edge of the tree. How can you optimally schedule the calls?
The Story Begins
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Bell Labs in New Jersey (Spring 1981)
John Klincewicz: Suppose you are routing phone calls in a tree network. Two calls interfere if they share an edge of the tree. How can you optimally schedule the calls?
The Story Begins
• A call is a path between a pair of nodes.• A typical example of a type of intersection graph.• Intersection here means “share an edge”.•Coloring this intersection graph is scheduling the calls.
An Olive Tree Network
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Edge Intersection Graphs of Paths in a Tree (EPT graphs)
tree communication network connecting different places
if two of these paths overlap,
they conflict and cannot use the
same resource at the same time.
Two types of intersections – share an edge vs share a node
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EPT graphs
EPT graph
share an edge
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VPT graphs
VPT graph
share a node
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Some Interesting Theorems VPT graphs are chordal EPT graphs are NOT chordal
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Some Interesting TheoremsVPT graphs are chordal
Buneman, Gavril, Wallace (early 1970's)
G is the vertex intersection graph of subtrees of a tree if and only if it is a chordal graph.
McMorris & Shier (1983)
A graph G is a vertex intersection graph of distinct subtrees of a star if and only if both G and its complement are chordal.
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Some Interesting TheoremsEPT graphs are NOT chordal
An EPT representation of C6
called a “6-pie”.6
3
2
1
4
5
Chordless cycles have a unique EPT representation.
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Algorithmic Complexity Results
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Some Interesting Theorems
Folklore (1970’s)
Every graph G is the edge intersection graph of distinct subtrees of a star.
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Degree 3 host trees (continued)
Theorem (1985): All four classes are equivalent:
chordal EPT deg3 EPT
VPT EPT deg3 VPT
What about degree 4?
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Degree 3 host trees (continued)
Theorem (1985): All four classes are equivalent:
chordal EPT deg3 EPT
VPT EPT deg3 VPT
Theorem (2005) [Golumbic, Lipshteyn, Stern]:
weakly chordal EPT deg4 EPT
Degree 4 host trees
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Definition Weakly Chordal Graph
No induced Cm for m 5, and
no induced Cm for m 5.
Weakly Chordal Graphs
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The Story Continues
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The Interval Graph Sandwich Problem
Interval problems with missing edges Benzer’s original problem
partial intersection data Is it consistent ?
Complete data would be recognition interval graphs (polynomial)
Partial data needs a different model and is NP-complete
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Interval Graph Sandwich Problem given a partially specified graph
E1 required edges
E2 optional edges
E3 forbidden edges
Can you fill-in some of the optional edges,
so that the result will be an interval graph? Golumbic & Shamir (1993): NP-Complete
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Interval Probe Graphs
A special tractable case of interval sandwich Computational biology motivated
Interval probe graph: vertices are partitioned P probes & N non-probes (independent set) can fill-in some of the N x N edges,
so that the result will be an interval graph
Motivation
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Example: Interval Probe GraphsNon-Probes are white
Probe graph NOT a Probe graph no matter how you partition vertices!
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Tolerance Graphs What if you only have 3 classrooms? Cancel a Lecture? or show Tolerance?
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Tolerance GraphsMeasured intersection:
small, or ``tolerable’’ amount of overlap, may be ignored does NOT produce an edge
at least one of them has to be ``bothered’’
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Tolerance Graphs
Assignment of positive numbers
{tv} (v V) such that
vw E if and only if | Iv Iw | min {tv , tw}
Measured intersection:
small, or ``tolerable’’ amount of overlap, may be ignored does NOT produce an edge
at least one of them has to be ``bothered’’
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Tolerance Graphs: Example
c and f will no longer conflict
| Ic If | < 60 = min {tc , tf}
More on Algorithmic Graph Theory