Algorithmic Graph Theory and its Applications

1. Algorithmic Graph Theory and its Applications Martin Charles Golumbic Algorithmic Graph Theory

2. Introduction • Intersection Graphs • Interval Graphs • Greedy Coloring • The Berge Mystery Story • Other Structure Families of Graphs • Graph Sandwich Problems • Probe Graphs and Tolerance Graphs Algorithmic Graph Theory

3. Theconcept of an intersection graph • applications in computation • operations research • molecular biology • scheduling • designing circuits • rich mathematical problems Algorithmic Graph Theory

4. 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 Algorithmic Graph Theory

5. 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 } Algorithmic Graph Theory

6. complement of a graph: interchanging the edges and the non-edges __ The complement G The original graph G Algorithmic Graph Theory

7. directed graph: edges have directions (possibly both directions) • orientation: exactly ONE direction per edge cyclic orientation acyclic orientation Algorithmic Graph Theory

8. Phase 1 Phase 2 Phase 3 Jan Feb Mar Apr May Jun July Sep Oct Nov Dec Interval Graphs The intersection graphs of intervals on a line: - create a vertex for each interval - connect vertices when their intervals intersect Task 5 Task 4 1 2 3 The interval graph G 4 5

9. 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 assignment VLSI design temporal reasoning in AI computer storage allocation Algorithmic Graph Theory

10. 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?

11. Scheduling Example (cont.)

12. Scheduling Example (graphs) • The interval graph • Its complement (disjointness)

13. 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 ! Algorithmic Graph Theory

14. Coloring Interval Graphs Algorithmic Graph Theory

15. Coloring Intervals (greedy) Algorithmic Graph Theory

16. 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. Algorithmic Graph Theory

17. Coloring Intervals (greedy) P(needs 4 colors) Algorithmic Graph Theory

18. Coloring Interval Graphs The clique at point P Algorithmic Graph Theory

19. Greedy the best we can do ! • Formally, • at least k colors are required • (because of the clique) • (2) greedy succeeded using k colors. • Therefore, • the solution is optimal. Q.E.D. Algorithmic Graph Theory

20. Characterizing Interval Graphs • Properties of interval graphs • How to recognize them • Their mathematical structure Algorithmic Graph Theory

21. 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 Algorithmic Graph Theory

22. 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! Algorithmic Graph Theory

23. Interval Graphs are Chordal Interval graphs may not contain chordless cycles! - i.e., they are chordal. Why? Algorithmic Graph Theory

24. Interval Graphs are Chordal Interval graphs may not contain chordless cycles! - i.e., they are chordal. Why? Algorithmic Graph Theory

25. The co-TRO Property The transitive orientation (TRO) of the complement i.e., the complement must have a TRO Not transitive ! Transitive ! Algorithmic Graph Theory

26. 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. Algorithmic Graph Theory

27. 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. Algorithmic Graph Theory

28. A Mystery in the Library 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:

29. The Facts: • 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?

30. Solving the Mystery The Testimony Graph Clue #1: Double arrows imply TRUTH

31. Solving the Mystery Undirected Testimony Graph cycle We know there is a lie, since {A, B, I, D} is a chordless 4-cycle.

32. 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!

33. Solving the Mystery One professor from the chordless 4-cycle must be a liar. 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 ?

34. 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.

35. 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. Algorithmic Graph Theory

36. Many other Families of Intersection Graphs Victor Klee, in a paper in 1969: ``What are the intersection graphs of arcs in a circle?’’ Algorithmic Graph Theory

37. Many other Families of Intersection Graphs Victor Klee, in a paper in 1969: ``What are the intersection graphs of arcs in a circle?“ Algorithmic Graph Theory

38. 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. Algorithmic Graph Theory

39. 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 Algorithmic Graph Theory

40. 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 Algorithmic Graph Theory

41. A small hierarchy Algorithmic Graph Theory

42. The Story Begins 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? Algorithmic Graph Theory

43. The Story Begins 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? Algorithmic Graph Theory

44. The Story Begins 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? An Olive Tree Network • 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. Algorithmic Graph Theory

45. 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 Algorithmic Graph Theory

46. EPT graphs EPT graph share an edge Algorithmic Graph Theory

47. VPT graphs VPT graph share a node Algorithmic Graph Theory

48. Some Interesting Theorems • VPT graphs are chordal • EPT graphs are NOT chordal Algorithmic Graph Theory

49. Some Interesting Theorems VPT 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. Algorithmic Graph Theory

50. Some Interesting Theorems EPT graphs are NOT chordal An EPT representation of C6 called a “6-pie”. 1 6 2 5 3 4 Chordless cycles have a unique EPT representation. Algorithmic Graph Theory