In graph theory, a class of graphs is said to have few cliques if every member of the class has a polynomial number of maximal cliques.[1] Certain generally NP-hard computational problems are solvable in polynomial time on such classes of graphs,[1][2] making graphs with few cliques of interest in computational graph theory, network analysis, and other branches of applied mathematics.[3] Informally, a family of graphs has few cliques if the graphs do not have a large number of large clusters.

Definition

A clique of a graph is a complete subgraph, while a maximal clique is a clique that is not properly contained in another clique. One can regard a clique as a cluster of vertices, since they are by definition all connected to each other by an edge. The concept of clusters is ubiquitous in data analysis, such as on the analysis of social networks. For that reason, limiting the number of possible maximal cliques has computational ramifications for algorithms on graphs or networks.

Formally, let be a class of graphs. If for every -vertex graph in , there exists a polynomial such that has maximal cliques, then is said to be a class of graphs with few cliques.[1]

Examples

References

  1. ^ a b c Prisner, E. (1995). Graphs with Few Cliques. In Y. Alavi & A. Schwenk (Eds.), Graph theory, combinatorics, and algorithms: proceedings of the Seventh Quadrennial International Conference on the Theory and Applications of Graphs (pp. 945–956). New York, N. Y: Wiley.
  2. ^ Rosgen, B., & Stewart, L. (2007). Complexity results on graphs with few cliques. Discrete Mathematics & Theoretical Computer Science, Vol. 9 no. 1 (Graph and Algorithms), 387. https://doi.org/10.46298/dmtcs.387
  3. ^ Fox, J., Roughgarden, T., Seshadhri, C., Wei, F., & Wein, N. (2020). Finding Cliques in Social Networks: A New Distribution-Free Model. SIAM Journal on Computing, 49(2), 448–464. https://doi.org/10.1137/18M1210459
  4. ^ Graham, R. L., Knuth, D. E., & Patashnik, O. (1994). Concrete mathematics: a foundation for computer science (2nd ed.). Reading, Mass: Addison-Wesley.
  5. ^ Moon, J. W., & Moser, L. (1965). On cliques in graphs. Israel Journal of Mathematics, 3(1), 23–28. https://doi.org/10.1007/BF02760024
  6. ^ Pahl, P. J., & Damrath, R. (2001). Mathematical foundations of computational engineering: a handbook. Berlin ; New York: Springer.
  7. ^ Gavril, F. (1974). The intersection graphs of subtrees in trees are exactly the chordal graphs. Journal of Combinatorial Theory, Series B, 16(1), 47–56. https://doi.org/10.1016/0095-8956(74)90094-X
  8. ^ Wood, D. R. (2007). On the Maximum Number of Cliques in a Graph. Graphs and Combinatorics, 23(3), 337–352. https://doi.org/10.1007/s00373-007-0738-8
  9. ^ Spinrad, J. P. (2003). Intersection and containment representations. In Efficient graph representations (pp. 31–53). Providence, R.I: American Mathematical Society.
  10. ^ Eppstein, D., Löffler, M., & Strash, D. (2010). Listing All Maximal Cliques in Sparse Graphs in Near-Optimal Time. In O. Cheong, K.-Y. Chwa, & K. Park (Eds.), Algorithms and Computation (Vol. 6506, pp. 403–414). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-17517-6_36
  11. ^ Brimkov, V. E., Junosza-Szaniawski, K., Kafer, S., Kratochvíl, J., Pergel, M., Rzążewski, P., et al. (2018). Homothetic polygons and beyond: Maximal cliques in intersection graphs. Discrete Applied Mathematics, 247, 263–277. https://doi.org/10.1016/j.dam.2018.03.046