Skip to main content
SpringerLink
Log in

A novel learning-based approach for efficient dismantling of networks

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Dismantling of complex networks is a problem to find a minimal set of nodes in which the removal leaves the network broken into connected components of sub-extensive size. It has a wide spectrum of important applications, including network immunization and network destruction. Due to its NP-hard computational complexity, this problem cannot be solved exactly with polynomial time. Traditional solutions, including manually-designed and considerably sub-optimal heuristic algorithms, and accurate message-passing ones, all suffer from low efficiency in large-scale problems. In this paper, we introduce a simple learning-based approach, CoreGQN, which seeks to train an agent that is able to smartly choose nodes that would accumulate the maximum rewards. CoreGQN is trained by hundreds of thousands self-plays on small synthetic graphs, and can then be able to generalize well on real-world networks across different types with different scales. Extensive experiments demonstrate that CoreGQN performs on par with the state-of-art algorithms at greatly reduced computational costs, suggesting that CoreGQN should be the better choice for practical network dismantling purposes.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. http://interactome.baderlab.org/data/Rolland-Vidal(Cell2014).psi.

  2. https://github.com/zhfkt/ComplexCi.

  3. http://power.itp.ac.cn/zhouhj/codes.html.

  4. https://github.com/abraunst/decycler.

  5. https://github.com/hcmidt/corehd.

  6. https://github.com/renxiaolong/Generalized-Network-Dismantling.

References

  1. Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47

    Article  MathSciNet  Google Scholar 

  2. Altarelli F, Braunstein A, Dall’Asta L, Zecchina R (2013) Large deviations of cascade processes on graphs. Phys Rev E 87(6):062,115

    Article  Google Scholar 

  3. Altarelli F, Braunstein A, Dall’Asta L, Zecchina R (2013) Optimizing spread dynamics on graphs by message passing. J Stat Mech Theory Exp 2013(09):P09,011

    Article  MathSciNet  Google Scholar 

  4. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  MathSciNet  Google Scholar 

  5. Bavelas A (1950) Communication patterns in task-oriented groups. J Acoust Soc Am 22(6):725–730

    Article  Google Scholar 

  6. Bello I, Pham H, Le QV, Norouzi M, Bengio S (2016) Neural combinatorial optimization with reinforcement learning. arXiv:1611.09940

  7. Braunstein A, Dall’Asta L, Semerjian G, Zdeborová L (2016) Network dismantling. Proc Natl Acad Sci 113(44):12368–12373

    Article  Google Scholar 

  8. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1–7):107–117

    Article  Google Scholar 

  9. Clusella P, Grassberger P, Pérez-Reche FJ, Politi A (2016) Immunization and targeted destruction of networks using explosive percolation. Phys Rev Lett 117(20):208,301

    Article  Google Scholar 

  10. Cohen R, Erez K, Ben-Avraham D, Havlin S (2001) Breakdown of the internet under intentional attack. Phys Rev Lett 86(16):3682

    Article  Google Scholar 

  11. Dai H, Dai B, Song L (2016) Discriminative embeddings of latent variable models for structured data. In: International conference on machine learning, pp 2702–2711

  12. Fan C, Liu Z, Lu X, Xiu B, Chen Q (2017) An efficient link prediction index for complex military organization. Physica A Stat Mech Appl 469:572–587

    Article  Google Scholar 

  13. Fan C, Sun Y, Zeng L, Liu YY, Chen M, Liu Z (2019) Dismantle large networks through deep reinforcement learning. In: ICLR representation learning on graphs and manifolds workshop

  14. Fan C, Xiao K, Xiu B, Lv G (2014) A fuzzy clustering algorithm to detect criminals without prior information. In: Proceedings of the 2014 IEEE/ACM international conference on advances in social networks analysis and mining, pp 238–243. IEEE Press

  15. Fan C, Zeng L, Ding Y, Chen M, Sun Y, Liu Z (2019) Learning to identify high betweenness centrality nodes from scratch: A novel graph neural network approach. arXiv:1905.10418

  16. Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41

    Article  Google Scholar 

  17. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1024–1034

  18. Hessel M, Modayil J, Van Hasselt H, Schaul T, Ostrovski G, Dabney W, Horgan D, Piot B, Azar M, Silver D (2018) Rainbow: combining improvements in deep reinforcement learning. In: Thirty-Second AAAI conference on artificial intelligence

  19. Janson S, Thomason A (2008) Dismantling sparse random graphs. Comb Probab Comput 17(2):259–264

    Article  MathSciNet  Google Scholar 

  20. Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 137–146. ACM

  21. Khalil E, Dai H, Zhang Y, Dilkina B, Song L (2017) Learning combinatorial optimization algorithms over graphs. In: Advances in neural information processing systems, pp 6348–6358

  22. Kunegis J (2013) Konect: the koblenz network collection. In: Proceedings of the 22nd international conference on world wide web, pp 1343–1350. ACM

  23. Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data 1(1):2

    Article  Google Scholar 

  24. Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123

    Article  MathSciNet  Google Scholar 

  25. Li Z, Chen Q, Koltun V (2018) Combinatorial optimization with graph convolutional networks and guided tree search. In: Advances in neural information processing systems, pp 539–548

  26. Ma Z, Li M, Wang Y (2019) Pan: Path integral based convolution for deep graph neural networks

  27. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529

    Article  Google Scholar 

  28. Morone F, Makse HA (2015) Influence maximization in complex networks through optimal percolation. Nature 524(7563):65

    Article  Google Scholar 

  29. Mugisha S, Zhou HJ (2016) Identifying optimal targets of network attack by belief propagation. Phys Rev E 94(1):012,305

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Ren XL, Gleinig N, Helbing D, Antulov-Fantulin N (2019) Generalized network dismantling. In: Proceedings of the national academy of sciences, p 201806108

  32. Schneider CM, Moreira AA, Andrade JS, Havlin S, Herrmann HJ (2011) Mitigation of malicious attacks on networks. Proc Natl Acad Sci 108(10):3838–3841

    Article  Google Scholar 

  33. Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. MIT Press, Cambridge

    MATH  Google Scholar 

  34. Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. In: ICLR

  35. Zdeborová L, Zhang P, Zhou HJ (2016) Fast and simple decycling and dismantling of networks. Sci Rep 6:37,954

    Article  Google Scholar 

  36. Zhou HJ (2013) Spin glass approach to the feedback vertex set problem. Eur Phys J B 86(11):455

    Article  MathSciNet  Google Scholar 

Download references

Author information

Author notes

  1. Changjun Fan and Li Zeng these authors have contributed equally to this work.

Authors and Affiliations

  1. College of Systems Engineering, National University of Defense Technology, Changsha, 410000, People’s Republic of China

    Changjun Fan, Li Zeng, Yanghe Feng, Guangquan Cheng, Jincai Huang & Zhong Liu

Authors
  1. Changjun Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanghe Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guangquan Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jincai Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanghe Feng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, C., Zeng, L., Feng, Y. et al. A novel learning-based approach for efficient dismantling of networks. Int. J. Mach. Learn. & Cyber. 11, 2101–2111 (2020). https://doi.org/10.1007/s13042-020-01104-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-020-01104-8

Keywords

Search

Navigation