Efficient algorithm to compute mutually connected components in interdependent networks.

Mutually connected components (MCCs) play an important role as a measure of resilience in the study of interdependent networks. Despite their importance, an efficient algorithm to obtain the statistics of all MCCs during the removal of links has thus far been absent. Here, using a well-known fully dynamic graph algorithm, we propose an efficient algorithm to accomplish this task. We show that the time complexity of this algorithm is approximately O(N(1.2)) for random graphs, which is more efficient than O(N(2)) of the brute-force algorithm. We confirm the correctness of our algorithm by comparing the behavior of the order parameter as links are removed with existing results for three types of double-layer multiplex networks. We anticipate that this algorithm will be used for simulations of large-size systems that have been previously inaccessible.

[1]  Alessandro Vespignani,et al.  Dynamical Processes on Complex Networks , 2008 .

[2]  Ronald Fagin,et al.  Proceedings of the thirty-seventh annual ACM symposium on Theory of computing , 2005, STOC 2005.

[3]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[4]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[5]  Albert-László Barabási,et al.  Evolution of Networks: From Biological Nets to the Internet and WWW , 2004 .

[6]  R. Rosenfeld Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[7]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[8]  W. Marsden I and J , 2012 .

[9]  David Bawden,et al.  Book Review: Evolution and Structure of the Internet: A Statistical Physics Approach. , 2006 .

[10]  Jari Saramäki,et al.  Temporal Networks , 2011, Encyclopedia of Social Network Analysis and Mining.