Bipartite dimension

In the mathematical fields of graph theory and combinatorial optimization, the bipartite dimension or biclique cover number of a graph G = (V, E) is the minimum number of bicliques (that is complete bipartite subgraphs), needed to cover all edges in E. A collection of bicliques covering all edges in G is called a biclique edge cover, or sometimes biclique cover. The bipartite dimension of G is often denoted by the symbol d(G).

## Contents

* 1 Example
* 2 Bipartite dimension formulas for some graphs
* 3 Computing the bipartite dimension
* 4 Applications
* 5 See also
* 6 References
* 7 External links

## Example

An example for a biclique edge cover is given in the following diagrams:

* Example for biclique edge cover
* A bipartite graph...

* ...and a covering with four bicliques

* the red biclique from the cover

* the blue biclique from the cover

* the green biclique from the cover

* the black biclique from the cover

## Bipartite dimension formulas for some graphs

The bipartite dimension of the n-vertex complete graph, Kn is ⌈log2n⌉.

The bipartite dimension of a 2n-vertex crown graph equals σ(n), where

σ(n)=min⁡{k∣n≤(k⌊k/2⌋)}

is the inverse function of the central binomial coefficient (de Caen Gregory).

The bipartite dimension of the n×m lattice graph is nm2−1, if m is even and n−1=k(m−1)+2ℓ for some integers 0≤ℓ<k; and is ⌊nm2⌋ otherwise (Guo Huynh).

(Fishburn Hammer) determine the bipartite dimension for some special graphs. For example, the path Pn has d(Pn)=⌊n/2⌋ and the cycle Cn has d(Cn)=⌈n/2⌉.

## Computing the bipartite dimension

The computational task of determining the bipartite dimension for a given graph G is an optimization problem. The decision problem for bipartite dimension can be phrased as:

INSTANCE: A graph G=(V,E) and a positive integer k.
QUESTION: Does G admit a biclique edge cover containing at most k bicliques?

This problem appears as problem GT18 in Garey and Johnson's classical book on NP-completeness, and is a rather straightforward reformulation of another decision problem on families of finite sets.

The set basis problem appears as problem SP7 in Garey and Johnson's book. Here, for a family 𝒮={S1,…,Sn} of subsets of a finite set 𝒰, a set basis for 𝒮 is another family of subsets ℬ={B1,…,Bℓ} of 𝒰, such that every set Si can be described as the union of some basis elements from ℬ. The set basis problem is now given as follows:

INSTANCE: A finite set 𝒰, a family 𝒮={S1,…,Sn} of subsets of 𝒰, and a positive integer k.
QUESTION: Does there exist a set basis of size at most k for 𝒮?

In its former formulation, the problem was proved to be NP-complete by (Orlin 1977), even for bipartite graphs. The formulation as a set basis problem was proved to be NP-complete earlier by (Stockmeyer 1975). The problem remains NP-hard even if we restrict our attention to bipartite graphs whose bipartite dimension is guaranteed to be at most O(log⁡n), with n denoting the size of the given problem instance (Gottlieb Savage). On the positive side, the problem is solvable in polynomial time on bipartite domino-free graphs (Amilhastre Janssen).

Regarding the existence of approximation algorithms, (Simon 1990) proved that the problem cannot be approximated well (assuming P ≠ NP). Indeed, the bipartite dimension was shown to be NP-hard to approximate within |V|1/3−ϵ for every fixed ϵ>0 by (Gruber Holzer) and NP-hard to approximate within |V|1−ϵ by (Chalermsook Heydrich), even on bipartite graphs.

In contrast, proving that the problem is fixed-parameter tractable is an exercise in designing kernelization algorithms, which appears as such in the textbook by (Downey Fellows). (Fleischner Mujuni) also provide a concrete bound on the size of the resulting kernel, which has meanwhile been improved by (Nor Hermelin). In fact, for a given bipartite graph on n vertices, it can be decided in time O(f(k))+n3 with f(k)=2k2k−1+3k whether its bipartite dimension is at most k (Nor Hermelin).

## Applications

The problem of determining the bipartite dimension of a graph appears in various contexts of computing. For instance, in computer systems, different users of a system can be allowed or disallowed accessing various resources. In a role-based access control system, a role provides access rights to a set of resources. A user can own multiple roles, and he has permission to access all resources granted by some of his roles. Also, a role can be owned by multiple users. The role mining problem is to find a minimum set of roles, such that for each user, his roles taken together grant access to all specified resources. The set of users together with the set of resources in the system naturally induces a bipartite graph, whose edges are permissions. Each biclique in this graph is a potential role, and the optimum solutions to the role mining problem are precisely the minimum biclique edge covers (Ene Horne).

A similar scenario is known in computer security, more specifically in secure broadcasting. In that setup, several messages need to be sent each to a set of receivers, over an insecure channel. Each message has to be encrypted using some cryptographic key that is known only to the intended receivers. Each receiver may possess multiple encryption keys, and each key will be distributed to multiple receivers. The optimum key generation problem is to find a minimum set of encryption keys for ensuring secure transmission. As above, the problem can be modeled using a bipartite graph whose minimum biclique edge covers coincide with the solutions to the optimum key generation problem (Shu Lee).

A different application lies in biology, where minimum biclique edge covers are used in mathematical models of human leukocyte antigen (HLA) serology (Nau Markowsky).

## See also

* List of NP-complete problems
* Intersection number (graph theory), the minimum number of cliques needed to cover the edges of a graph

## References

* Amilhastre, Jérôme; Janssen, Philippe; Vilarem, Marie-Catherine (1997), "Computing a minimum biclique cover is polynomial for bipartite domino-free graphs", Proceedings of the Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 5–7 January 1997, New Orleans, Louisiana., ACM/SIAM, pp. 36–42, ISBN 9780898713909, http://portal.acm.org/citation.cfm?id=314175
* Chalermsook, Parinya; Heydrich, Sandy; Holm, Eugenia; Karrenbauer, Andreas (2014), "Nearly Tight Approximability Results for Minimum Biclique Cover and Partition", European Symposium on Algorithms, https://link.springer.com/chapter/10.1007/978-3-662-44777-2_20
* de Caen, Dominique; Gregory, David A.; Pullman, Norman J. (1981), "The Boolean rank of zero-one matrices", in Cadogan, Charles C., 3rd Caribbean Conference on Combinatorics and Computing, Department of Mathematics, University of the West Indies, pp. 169–173 .
* Parameterized complexity, Springer, 1999, ISBN 0-387-94883-X .
* Ene, Alina; Horne, William G.; Milosavljevic, Nikola; Rao, Prasad; Schreiber, Robert; Tarjan, Robert Endre (2008), "Fast exact and heuristic methods for role minimization problems", in Ray, Indrakshi; Li, Ninghui, 13th ACM Symposium on Access Control Models and Technologies (SACMAT 2008), ACM, pp. 1–10 .
* "Bipartite dimensions and bipartite degrees of graphs", Discrete Mathematics 160 (1–3): 127–148, 1996, doi:10.1016/0012-365X(95)00154-O .
* Fleischner, Herbert; Mujuni, Egbert; Paulusma, Daniël; Szeider, Stefan (2009), "Covering graphs with few complete bipartite subgraphs", Theoretical Computer Science 410 (21–23): 2045–2053, doi:10.1016/j.tcs.2008.12.059 .
* Computers and Intractability: A Guide to the Theory of NP-Completeness, W.H. Freeman, 1979, ISBN 0-7167-1045-5 .
* Gottlieb, Lee-Ad J. (2005), "Efficient data storage in large nanoarrays", Theory of Computing Systems 38 (4): 503–536, doi:10.1007/s00224-004-1196-9 .
* Gruber, Hermann; Holzer, Markus (2007), "Inapproximability of Nondeterministic State and Transition Complexity Assuming P <> NP.", in Harju, Terjo; Karhumäki, Juhani; Lepistö, Arto, 11th International Conference on Developments in Language Theory (DLT 2007), LNCS, 4588, Turku, Finland: Springer, pp. 205–216, doi:10.1007/978-3-540-73208-2_21, ISBN 978-3-540-73207-5 .
* Guo, Krystal; Huynh, Tony; Macchia, Marco (2019), "The Biclique Covering Number of Grids", The Electronic Journal of Combinatorics 26 (4), doi:10.37236/8316 .
* Monson, Sylvia D.; Pullman, Norman J.; Rees, Rolf (1995), "A survey of clique and biclique coverings and factorizations of (0,1)-matrices", Bulletin of the ICA 14: 17–86 .
* Nau, D. S.; Markowsky, G.; Woodbury, M. A.; Amos, D. B. (1978), "A mathematical analysis of human leukocyte antigen serology", Mathematical Biosciences 40 (3–4): 243–270, doi:10.1016/0025-5564(78)90088-3, https://www.cs.umd.edu/~nau/papers/nau1978mathematical.pdf .
* Nor, Igor; Hermelin, Danny; Charlat, Sylvain; Engelstadter, Jan; Reuter, Max; Duron, Olivier; Sagot, Marie-France (2010), "Mod/Resc Parsimony Inference", Combinatorial Pattern Matching, Lecture Notes in Computer Science, 6129, pp. 202–213, doi:10.1007/978-3-642-13509-5_19, ISBN 978-3-642-13508-8
* "Contentment in graph theory: covering graphs with cliques", Indagationes Mathematicae 80 (5): 406–424, 1977, doi:10.1016/1385-7258(77)90055-5 .
* Shu, Guoqiang; Lee, David; Yannakakis, Mihalis (2006), "A note on broadcast encryption key management with applications to large scale emergency alert systems.", 20th International Parallel and Distributed Processing Symposium (IPDPS 2006), IEEE .
* Simon, Hans-Ulrich (1990), "On Approximate Solutions for Combinatorial Optimization Problems", SIAM Journal on Discrete Mathematics 3 (2): 294–310, doi:10.1137/0403025 .
* The set basis problem is NP-complete, Technical Report RC-5431, IBM, 1975 .

## External links

* blog entry about bipartite dimension by David Eppstein

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