Skip to main content

Can We Create Large k-Cores by Adding Few Edges?

  • Conference paper
  • First Online:
Computer Science – Theory and Applications (CSR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10846))

Included in the following conference series:

Abstract

The notion of a k-core, defined by Seidman [’83], has turned out to be useful in analyzing network structures. The k-core of a given simple and undirected graph is the maximal induced subgraph such that each vertex in it has degree at least k. Hence, finding a k-core helps to identify a (core) community where each entity is related to at least k other entities. One can find the k-core of a given graph in polynomial time, by iteratively deleting each vertex of degree less than k. Unfortunately, this iterative dropping out of vertices can sometimes lead to unraveling of the entire network; e.g., Schelling [’78] considered the extreme example of a path with \(k = 2\), where indeed the whole network unravels.

In order to avoid this unraveling, we would like to edit the network in order to maximize the size of its k-core. Formally, we introduce the Edge k-Core problem (EKC): given a graph G, a budget b, and a goal p, can at most b edges be added to G to obtain a k-core containing at least p vertices? First we show the following dichotomy: EKC is polytime solvable for \(k \le 2\) and NP-hard for \(k \ge 3\). Then, we show that EKC is W[1]-hard even when parameterized by \(b + k + p\). In searching for an FPT algorithm, we consider the parameter “treewidth”, and design an FPT algorithm for EKC which runs in time \((k+\mathbf{tw })^{O(\mathbf{tw }+b)}\cdot \text {poly}(n)\), where \(\mathbf{tw }\) is the treewidth of the input graph. Even though an extension of Courcelle’s theorem [Arnborg et al., J. Algorithms ’91] can be used to show FPT for EKC parameterized by \(\mathbf{tw }+k+b\), we obtain a much faster running time as compared to Courcelle’s theorem (which needs a tower of exponents) by designing a dynamic programming algorithm which needs to take into account the fact that newly added edges might have endpoints in different bags which cross the separator.

R. Chitnis—Supported by ERC grant CoG 647557 “Small Summaries for Big Data”. Part of this work was done when the author was at the Weizmann Institute of Science and supported by Israel Science Foundation grant #897/13.

N. Talmon—Part of this work was done when the author was at the Weizmann Institute of Science.

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.

    That is, distinguishing whether the size of the optimal k-core is O(b) or \(\varOmega (n)\).

  2. 2.

    Proofs of results marked with [\(\star \)] are deferred to the full version of the paper due to lack of space.

References

  1. Alvarez-Hamelin, J.I., Dall’Asta, L., Barrat, A., Vespignani, A.: Large scale networks fingerprinting and visualization using the \(k\)-core decomposition. In: NIPS 2005, pp. 41–50 (2005)

    Google Scholar 

  2. Batagelj, V., Mrvar, A., Zaveršnik, M.: Partitioning approach to visualization of large graphs. In: Kratochvíyl, J. (ed.) GD 1999. LNCS, vol. 1731, pp. 90–97. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-46648-7_9

    Chapter  Google Scholar 

  3. Baur, M., Brandes, U., Gaertler, M., Wagner, D.: Drawing the AS graph in 2.5 dimensions. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, pp. 43–48. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31843-9_6

    Chapter  MATH  Google Scholar 

  4. Bhawalkar, K., Kleinberg, J.M., Lewi, K., Roughgarden, T., Sharma, A.: Preventing unraveling in social networks: the anchored \(k\)-core problem. SIAM J. Discret. Math. 29(3), 1452–1475 (2015)

    Article  MathSciNet  Google Scholar 

  5. Bodlaender, H.L., Koster, A.: Combinatorial optimization on graphs of bounded treewidth. Comput. J. 51(3), 255–269 (2008)

    Article  Google Scholar 

  6. Chitnis, R., Fomin, F.V., Golovach, P.A.: Parameterized complexity of the anchored \(k\)-core problem for directed graphs. Inf. Comput. 247, 11–22 (2016)

    Article  MathSciNet  Google Scholar 

  7. Chitnis, R.H., Fomin, F.V., Golovach, P.A.: Preventing unraveling in social networks gets harder. In: Proceedings of AAAI 2013 (2013)

    Google Scholar 

  8. Chwe, M.: Structure and strategy in collective action 1. Am. J. Sociol. 105(1), 128–156 (1999)

    Article  MathSciNet  Google Scholar 

  9. Chwe, M.: Communication and coordination in social networks. Rev. Econ. Stud. 67(1), 1–16 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  10. Dezső, Z., Barabási, A.: Halting viruses in scale-free networks. Phys. Rev. E 65(5), 055103 (2002)

    Article  Google Scholar 

  11. Downey, R.G., Fellows, M.R.: Fundamentals of Parameterized Complexity. Texts in Computer Science. Springer, Heidelberg (2013). https://doi.org/10.1007/978-1-4471-5559-1

    Book  MATH  Google Scholar 

  12. Du, N., Wu, B., Pei, X., Wang, B., Xu, L.: Community detection in large-scale social networks. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 16–25. ACM (2007)

    Google Scholar 

  13. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  14. Gutiérrez-Bunster, T., Stege, U., Thomo, A., Taylor, J.: How do biological networks differ from social networks? (an experimental study). In: ASONAM, pp. 744–751 (2014)

    Google Scholar 

  15. Kloks, T. (ed.): Treewidth, Computations and Approximations. LNCS, vol. 842. Springer, Heidelberg (1994). https://doi.org/10.1007/BFb0045375

    Book  MATH  Google Scholar 

  16. Korovaiko, N., Thomo, A.: Trust prediction from user-item ratings. Soc. Netw. Anal. Min. 3(3), 749–759 (2013)

    Article  Google Scholar 

  17. Mikolajczyk, R.T., Kretzschmar, M.: Collecting social contact data in the context of disease transmission: prospective and retrospective study designs. Soc. Netw. 30(2), 127–135 (2008)

    Article  Google Scholar 

  18. Pandit, S., Chau, D.H., Wang, S., Faloutsos, C.: NetProbe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the 16th International Conference on World Wide Web, pp. 201–210. ACM (2007)

    Google Scholar 

  19. Papadopoulos, S., Kompatsiaris, Y., Vakali, A., Spyridonos, P.: Community detection in social media. Data Min. Knowl. Discov. 24(3), 515–554 (2012)

    Article  Google Scholar 

  20. Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86(14), 3200–3203 (2001)

    Article  Google Scholar 

  21. Sääskilahti, P.: Monopoly pricing of social goods. Technical report. University Librray of Munich (2007)

    Google Scholar 

  22. Schelling, T.: Micromotives and Macrobehavior. W. W. Norton, New York City (1978)

    Google Scholar 

  23. Seidman, S.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)

    Article  MathSciNet  Google Scholar 

  24. Yang, W.-S., Dia, J.-B.: Discovering cohesive subgroups from social networks for targeted advertising. Expert Syst. Appl. 34(3), 2029–2038 (2008)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Fedor Fomin, Petr Golovach, and Bart M.P. Jansen for helpful discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rajesh Chitnis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chitnis, R., Talmon, N. (2018). Can We Create Large k-Cores by Adding Few Edges?. In: Fomin, F., Podolskii, V. (eds) Computer Science – Theory and Applications. CSR 2018. Lecture Notes in Computer Science(), vol 10846. Springer, Cham. https://doi.org/10.1007/978-3-319-90530-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90530-3_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90529-7

  • Online ISBN: 978-3-319-90530-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics