Bounds on isolated scattering number

2021 ◽  
Vol 62 ◽  
pp. C72-C83
Author(s):  
Marcin Jurkiewicz

The isolated scattering number is a parameter that measures the vulnerability of networks. This measure is bounded by formulas depending on the independence number. We present new bounds on the isolated scattering number that can be calculated in polynomial time. References Z. Chen, M. Dehmer, F. Emmert-Streib, and Y. Shi. Modern and interdisciplinary problems in network science: A translational research perspective. CRC Press, 2018. doi: 10.1201/9781351237307 P. Erdős and T. Gallai. On the minimal number of vertices representing the edges of a graph. Magyar Tud. Akad. Mat. Kutató Int. Közl. 6 (1961), pp. 181–203. url: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.210.7468 J. Harant and I. Schiermeyer. On the independence number of a graph in terms of order and size. Discrete Math. 232.1–3 (2001), pp. 131–138. doi: 10.1016/S0012-365X(00)00298-3 E. Korach, T. Nguyen, and B. Peis. Subgraph characterization of red/blue-split graph and Kőnig Egerváry graphs. Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithms. ACM, New York, 2006, pp. 842–850. doi: 10.1145/1109557.1109650 F. Li, Q. Ye, and Y. Sun. Proceedings of the 2016 Joint Conference of ANZIAM and Zhejiang Provincial Applied Mathematics Association, ANZPAMS-2016. Ed. by P. Broadbridge, M. Nelson, D. Wang, and A. J. Roberts. Vol. 58. ANZIAM J. 2017, E81–E97. doi: 10.21914/anziamj.v58i0.10993 F. Li, Q. Ye, and X. Zhang. Isolated scattering number of split graphs and graph products. ANZIAM J. 58.3-4 (2017), pp. 350–358. doi: 10.1017/S1446181117000062 E. R. Scheinerman and D. H. Ullman. Fractional graph theory. Dover Publications, 2011. url: https://www.ams.jhu.edu/ers/wp-content/uploads/2015/12/fgt.pdf S. Y. Wang, Y. X. Yang, S. W. Lin, J. Li, and Z. M. Hu. The isolated scattering number of graphs. Acta Math. Sinica (Chin. Ser.) 54.5 (2011), pp. 861–874. url: http://www.actamath.com/EN/abstract/abstract21097.shtml M. Xiao and H. Nagamochi. Exact algorithms for maximum independent set. Inform. and Comput. 255, Part 1 (2017), pp. 126–146. doi: 10.1016/j.ic.2017.06.001

2018 ◽  
Vol 10 (05) ◽  
pp. 1850069
Author(s):  
Nader Jafari Rad ◽  
Elahe Sharifi

The independence number of a graph [Formula: see text], denoted by [Formula: see text], is the maximum cardinality of an independent set of vertices in [Formula: see text]. [Henning and Löwenstein An improved lower bound on the independence number of a graph, Discrete Applied Mathematics  179 (2014) 120–128.] proved that if a connected graph [Formula: see text] of order [Formula: see text] and size [Formula: see text] does not belong to a specific family of graphs, then [Formula: see text]. In this paper, we strengthen the above bound for connected graphs with maximum degree at least three that have a non-cut-vertex of maximum degree. We show that if a connected graph [Formula: see text] of order [Formula: see text] and size [Formula: see text] has a non-cut-vertex of maximum degree then [Formula: see text], where [Formula: see text] is the maximum degree of the vertices of [Formula: see text]. We also characterize all connected graphs [Formula: see text] of order [Formula: see text] and size [Formula: see text] that have a non-cut-vertex of maximum degree and [Formula: see text].


2017 ◽  
Vol 255 ◽  
pp. 126-146 ◽  
Author(s):  
Mingyu Xiao ◽  
Hiroshi Nagamochi

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yaoxin Li ◽  
Jing Liu ◽  
Guozheng Lin ◽  
Yueyuan Hou ◽  
Muyun Mou ◽  
...  

AbstractIn computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure, such that the designed objective function is optimized under some constraints. However, these problems are notorious for their hardness to solve, because most of them are NP-hard or NP-complete. Although traditional general methods such as simulated annealing (SA), genetic algorithms (GA), and so forth have been devised to these hard problems, their accuracy and time consumption are not satisfying in practice. In this work, we proposed a simple, fast, and general algorithm framework based on advanced automatic differentiation technique empowered by deep learning frameworks. By introducing Gumbel-softmax technique, we can optimize the objective function directly by gradient descent algorithm regardless of the discrete nature of variables. We also introduce evolution strategy to parallel version of our algorithm. We test our algorithm on four representative optimization problems on graph including modularity optimization from network science, Sherrington–Kirkpatrick (SK) model from statistical physics, maximum independent set (MIS) and minimum vertex cover (MVC) problem from combinatorial optimization on graph, and Influence Maximization problem from computational social science. High-quality solutions can be obtained with much less time-consuming compared to the traditional approaches.


1977 ◽  
Vol 6 (3) ◽  
pp. 537-546 ◽  
Author(s):  
Robert Endre Tarjan ◽  
Anthony E. Trojanowski

Sign in / Sign up

Export Citation Format

Share Document