Graph bandwidth explained

In graph theory, the graph bandwidth problem may be visualized as placing the vertices of a given graph at distinct integer positions along the number line so that the length of the longest edge is minimized. Such placement is called linear graph arrangement, linear graph layout or linear graph placement.It may be formalized as labeling the

vertices of a graph with distinct integers so that the quantity is minimized, where is the edge set of .^[1] and the cost function to be minimized is the product of weight with length, .

In terms of matrices, the (unweighted) graph bandwidth is the minimal bandwidth of a symmetric matrix which is an adjacency matrix of the graph.The bandwidth may also be defined as one less than the maximum clique size in a proper interval supergraph of the given graph, chosen to minimize its clique size.^[2]

Bandwidth formulas for some graphs

For several families of graphs, the bandwidth

is given by an explicit formula. on vertices is 1, and the bandwidth of a complete graph is . For the complete bipartite graph ,$$\backslash varphi(K\_)\; =\; \backslash lfloor\; (m-1)/2\backslash rfloor+n,$$ assuming . As a special case of this formula, the star graph on vertices has bandwidth .^[3] on vertices the bandwidth is^[4] $$\backslash varphi(Q\_n)=\backslash sum\_^\; \backslash binom.$$

The bandwidth of the

square grid graph , that is, the Cartesian product of two path graphs on and vertices, is equal to .^[5]

Bounds

The bandwidth of a graph can be bounded in terms of various other graph parameters. For instance, letting χ(G) denote the chromatic number of G,

letting diam(G) denote the diameter of G, the following inequalities hold:^[1]

where

is the number of vertices in .

If a graph G has bandwidth k, then its pathwidth is at most k,^[2] and its tree-depth is at most k log(n/k).^[6] In contrast, as noted in the previous section, the star graph S_k, a structurally very simple example of a tree, has comparatively large bandwidth. Observe that the pathwidth of S_k is 1, and its tree-depth is 2.

Some graph families of bounded degree have sublinear bandwidth: if T is a tree of maximum degree at most ∆, then ^[7]

More generally, for planar graphs of bounded maximum degree at most ∆, a similar bound holds:^[8]

Computing the bandwidth

Both the unweighted and weighted versions are special cases of the quadratic bottleneck assignment problem.The bandwidth problem is NP-hard, even for some special cases.^[9] Regarding the existence of efficientapproximation algorithms, it is known that the bandwidth is NP-hard to approximate within any constant, and this even holds when the input graphs are restricted to caterpillar trees with maximum hair length 2.^[10] For arbitrary graphs with

vertices, the best approximation ratio known is , using semidefinite programming.^[11] For the case of dense graphs, a 3-approximation algorithm is known.^[12] On the other hand, a number of polynomially-solvable special cases are known.^[13] A heuristic algorithm for obtaining linear graph layouts of low bandwidth is the Cuthill–McKee algorithm. Fast multilevel algorithm for graph bandwidth computation was proposed in.^[14]

Applications

The interest in this problem comes from some application areas.

One area is sparse matrix/band matrix handling, and general algorithms from this area, such as Cuthill–McKee algorithm, may be applied to find approximate solutions for the graph bandwidth problem.

Another application domain is in electronic design automation. In standard cell design methodology, typically standard cells have the same height, and their placement is arranged in a number of rows. In this context, graph bandwidth problem models the problem of placement of a set of standard cells in a single row with the goal of minimizing the maximal propagation delay (which is assumed to be proportional to wire length).

See also

• Cutwidth and pathwidth, different NP-complete optimization problems involving linear layouts of graphs.

External links

• Minimum bandwidth problem, in: Pierluigi Crescenzi and Viggo Kann (eds.), A compendium of NP optimization problems. Accessed May 26, 2010.

Notes and References

1. Chinn . P. Z. . Phyllis Chinn. Chvátalová . J. . Dewdney . A. K. . Alexander Dewdney. Gibbs . N. E. . The bandwidth problem for graphs and matrices—a survey . Journal of Graph Theory . 6 . 223–254. 1982 . 3 . 10.1002/jgt.3190060302 .
2. Pathwidth, bandwidth, and completion problems to proper interval graphs with small cliques . 1996 . . 540–561 . 25 . 3 . Kaplan . Haim . Shamir . Ron . 10.1137/s0097539793258143.
3. Chvátal . Václav . Václav Chvátal . 10.21136/CMJ.1970.100949 . 10338.dmlcz/100949 . 1 . Czechoslovak Mathematical Journal . 266791 . 109–111 . A remark on a problem of Harary . 20 . 1970.
4. Harper . L. H. . 10.1016/S0021-9800(66)80059-5 . . 200192 . 385–393 . Optimal numberings and isoperimetric problems on graphs . 1 . 1966.
5. Chvátalová . Jarmila . 10.1016/0012-365X(75)90039-4 . . 427150 . 249–253 . Optimal labelling of a product of two paths . 11 . 1975.
6. On Balanced Separators, Treewidth, and Cycle Rank . 2012 . Gruber . Hermann . Journal of Combinatorics . 669–682 . 3 . 4 . 10.4310/joc.2012.v3.n4.a5. 1012.1344 .
7. Book: Chung , Fan R. K. . Fan Chung . 1988 . Labelings of Graphs . Beineke . Lowell W. . Wilson . Robin J. . Selected Topics in Graph Theory . Academic Press . 151–168 . 978-0-12-086203-0 .
8. Böttcher . J. . Pruessmann . K. P. . Taraz . A. . Würfl . A. . Bandwidth, expansion, treewidth, separators and universality for bounded-degree graphs . 10.1016/j.ejc.2009.10.010 . European Journal of Combinatorics . 31 . 1217–1227 . 2010 . 5 . 0910.3014 .
9. Book: Garey , M.R. . Michael Garey . Johnson, D.S. . David S. Johnson . . 1979 . W.H. Freeman . New York . 0-7167-1045-5 . Problem GT40.
10. Dubey . C. . Feige . U. . Unger . W. . Hardness results for approximating the bandwidth . Journal of Computer and System Sciences . 77 . 62–90. 2010 . 10.1016/j.jcss.2010.06.006 . free .
11. Book: Dunagan . John . Vempala . Santosh S. . Goemans . Michel X. . Jansen . Klaus . Rolim . José D. P. . Trevisan . Luca . On Euclidean embeddings and bandwidth minimization . 10.1007/3-540-44666-4_26 . 229–240 . Springer . Lecture Notes in Computer Science . Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques, 4th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2001 and 5th International Workshop on Randomization and Approximation Techniques in Computer Science, RANDOM 2001 Berkeley, CA, USA, August 18–20, 2001, Proceedings . 2129 . 2001.
12. Karpinski . Marek . Wirtgen . Jürgen . Zelikovsky . Aleksandr . An Approximation Algorithm for the Bandwidth Problem on Dense Graphs . Electronic Colloquium on Computational Complexity . 4 . 17 . 1997 .
13. Book: Feige, Uriel . Uriel Feige . Halldórsson . Magnús M. . Coping with the NP-hardness of the graph bandwidth problem . 10.1007/3-540-44985-X_2 . 10–19 . Springer . Lecture Notes in Computer Science . Algorithm Theory – SWAT 2000, 7th Scandinavian Workshop on Algorithm Theory, Bergen, Norway, July 5–7, 2000, Proceedings . 1851 . 2000.
14. Ilya Safro and Dorit Ron and Achi Brandt . Multilevel Algorithms for Linear Ordering Problems . ACM Journal of Experimental Algorithmics . 2008 . 1.4–1.20 . 13 . 10.1145/1412228.1412232 .