Skip to main content

Generalized Disk Graphs

  • Conference paper
  • First Online:
Algorithms and Data Structures (WADS 2021)

Abstract

A graph G is a Generalized Disk Graph if for some dimension \(\eta \ge 1\), a non-decreasing sub-linear function f and natural number t, each vertex \(v_i\) can be assigned a length \(l_i\) and set \(P_i \subseteq \mathbb {R}^\eta \) of t points such that \(v_iv_j\) is an edge of G if and only if \(l_i \le l_j\) and \(d(P_i, P_j) \le l_if(l_j/l_i)\,+\,l_if(1)\), where \(d(\cdot , \cdot )\) is the least distance between points in either set. Generalized disk graphs were introduced as a model of wireless network interference and have been shown to be dramatically more accurate than disk graphs or other previously known graph classes. However, their properties have not been studied extensively before.

We give a geometric representation of these graphs as intersection graphs of convex shapes, relate them to other geometric intersection graph classes, and solve several important optimization problems on these graphs using the geometric representation; either exactly (in two-dimensions) or approximately (in higher dimensions).

Work supported by grant 174484-051 from Icelandic Research Fund and grant 208348-0091 from Icelandic Student Innovation Fund.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We sometimes consider \(f(x)=x\) as an example; it is not sublinear but it satisfies all important properties we need (e.g., it is grounded w.r.t. hyperplane \(h=1\)).

References

  1. Agarwal, P.K., Katz, M.J., Sharir, M.: Computing depth orders for fat objects and related problems. Comput. Geom. 5(4), 187–206 (1995)

    Article  MathSciNet  Google Scholar 

  2. Alt, H., et al.: Approximate motion planning and the complexity of the boundary of the union of simple geometric figures. Algorithmica 8(1), 391–406 (1992)

    Article  MathSciNet  Google Scholar 

  3. Aronov, B., Bar-On, G., Katz, M.J.: Resolving SINR queries in a dynamic setting. In: Chatzigiannakis, I., Kaklamanis, C., Marx, D., Sannella, D. (eds.), 45th International Colloquium on Automata, Languages, and Programming, ICALP 2018, 9–13 July 2018, Prague, Czech Republic, volume 107 of LIPIcs, pp. 145:1–145:13. Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)

    Google Scholar 

  4. Ásgeirsson, E.I., Halldórsson, M.M., Tonoyan, T.: Universal framework for wireless scheduling problems. In: 44th International Colloquium on Automata, Languages, and Programming, ICALP 2017, 10–14 July 2017, Warsaw, Poland, pp. 129:1–129:15 (2017)

    Google Scholar 

  5. Catanzaro, D., et al.: Max point-tolerance graphs. Discret. Appl. Math. 216, 84–97 (2017)

    Article  MathSciNet  Google Scholar 

  6. Chan, T.M.: Polynomial-time approximation schemes for packing and piercing fat objects. J. Algorithms 46(2), 178–189 (2003)

    Article  MathSciNet  Google Scholar 

  7. Chan, T.M., Har-Peled, S.: Approximation algorithms for maximum independent set of pseudo-disks. Discret. Comput. Geom. 48(2), 373–392 (2012)

    Article  MathSciNet  Google Scholar 

  8. Clark, B.N., Colbourn, C.J., Johnson, D.S.: Unit disk graphs. Discret. Math. 86(1–3), 165–177 (1990)

    Article  MathSciNet  Google Scholar 

  9. Edmonds, J., Karp, R.M.: Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM 19(2), 248–264 (1972)

    Article  Google Scholar 

  10. Efrat, A., Katz, M.J., Nielsen, F., Sharir, M.: Dynamic data structures for fat objects and their applications. Comput. Geom. 15(4), 215–227 (2000)

    Article  MathSciNet  Google Scholar 

  11. Erlebach, T., Fiala, J.: Independence and coloring problems on intersection graphs of disks. In: Bampis, E., Jansen, K., Kenyon, C. (eds.) Efficient Approximation and Online Algorithms. LNCS, vol. 3484, pp. 135–155. Springer, Berlin (2006). https://doi.org/10.1007/11671541_5

    Chapter  MATH  Google Scholar 

  12. Erlebach, T., Jansen, K., Seidel, E.: Polynomial-time approximation schemes for geometric intersection graphs. SIAM J. Comput. 34, 1302–1323 (2005)

    Article  MathSciNet  Google Scholar 

  13. Halldorsson, M.M., Tonoyan, T.: How well can graphs represent wireless interference? In: Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing. STOC 2015, pp. 635–644. Association for Computing Machinery, New York, NY, USA (2015)

    Google Scholar 

  14. Jamison, R.E., Mulder, H.M.: Tolerance intersection graphs on binary trees with constant tolerance 3. Discret. Math. 215(1), 115–131 (2000)

    Article  MathSciNet  Google Scholar 

  15. Kammer, F., Tholey, T.: Approximation algorithms for intersection graphs. Algorithmica 68(2), 312–336 (2012)

    Article  MathSciNet  Google Scholar 

  16. Kantor, E., Lotker, Z., Parter, M., Peleg, D.: The topology of wireless communication. J. ACM 62(5), 37:1-37:32 (2015)

    Article  MathSciNet  Google Scholar 

  17. Keil, J.M., Mitchell, J.S.B., Pradhan, D., Vatshelle, M.: An algorithm for the maximum weight independent set problem on outerstring graphs. Comput. Geom. 60, 19–25 (2017). The Twenty-Seventh Canadian Conference on Computational Geometry August 2015

    Article  MathSciNet  Google Scholar 

  18. Kratochvíl, J.: String graphs. I. The number of critical nonstring graphs is infinite. J. Comb. Theory, Ser. B 52(1), 53–66 (1991)

    Google Scholar 

  19. Moscibroda, T., Wattenhofer, R.: The complexity of connectivity in wireless networks. In: INFOCOM, pp. 1–13. IEEE (2006)

    Google Scholar 

  20. Paul, S.: On characterizing proper-max-point tolerance graphs (2020)

    Google Scholar 

  21. Soto, M., Caro, C.T.: p-BOX: a new graph model. Discret. Math. Theor. Comput. Sci. 17(1), 169–186 (2015)

    MathSciNet  MATH  Google Scholar 

  22. van Kreveld, M.: On fat partitioning, fat covering and the union size of polygons. Comput. Geom. 9(4), 197–210 (1998)

    Article  MathSciNet  Google Scholar 

  23. Ye, Y., Borodin, A.: Elimination graphs. ACM Trans. Algorithms 8, 2 (2012)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Arnþórsson, Í.M., Chaplick, S., Gylfason, J.S., Halldórsson, M.M., Reynisson, J.M., Tonoyan, T. (2021). Generalized Disk Graphs. In: Lubiw, A., Salavatipour, M., He, M. (eds) Algorithms and Data Structures. WADS 2021. Lecture Notes in Computer Science(), vol 12808. Springer, Cham. https://doi.org/10.1007/978-3-030-83508-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-83508-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-83507-1

  • Online ISBN: 978-3-030-83508-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics