References
- C. Ahlberg, C. Williamson, and B. Shneiderman, Dynamic queries for information exploration: An implementation and evaluation, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1992, pp. 619–626.
- N.K. Ahmed, N. Duffield, J. Neville, and R. Kompella, Graph sample and hold: A framework for big-graph analytics, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2014, pp. 1446–1455.
- N.K. Ahmed, J. Neville, and R. Kompella, Reconsidering the foundations of network sampling, Proceedings of the 2nd Workshop on Information in Networks, Sept. 2010.
- N.K. Ahmed, J. Neville, R.A. Rossi, N.G. Duffield, and T.L. Willke, Graphlet decomposition: framework, algorithms, and applications. Knowl. Inf. Syst. 50 (2017), pp. 689–722.
- T. Angskun, G. Bosilca, J. Dongarra, Binomial graph: A scalable and fault-tolerant logical network topology, in The Fifth International Symposium on Parallel and Distributed Processing and Applications (ISPA'07), Lecture Notes in Computer Science, Vol. 4742, pp. 471–482, Springer, Berlin, Heidelberg, 2007.
- J.A. Bondy, U.S.R. Murty, Graph Theory with Applications, North Holland, New York, 1976, pp. 12–21.
- A.E. Brouwer, W.H. Haemers, Spectra of Graphs, Springer-Verlag, New York, 2012.
- M. Dehmer, X. Li, and Y. Shi, Connections between generalized graph entropies and graph energy, Complexity, 21 (2015), pp.35–41.
- E. Estrada and M. Benzi, What is the meaning of the graph energy after all? Discrete Appl. Math. 230 (2017), pp. 71–77.
- M. Girvan and M.E.J. Newman, Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99 (2002), pp. 7821–7826.
- I. Gutman, Acyclic systems with extremal Hückel π-electron energy. Theor. Chem. Acc.: Theory Comput. Model. (Theor. Chim. Acta) 45 (1977), pp. 79–87.
- I. Gutman, The energy of a graph. Ber Math–Statist Sekt Forschungsz Graz 103 (1978), pp. 1–22.
- A.M. Hinz, S. Klavž, and S. Sabrina Zemljič, A survey and classification of Sierpiński-type graphs. Discrete Appl. Math. 217 (2017), pp. 565–600.
- S. Khorshidi, M. Al Hasan, G. Mohler, M.B. Short, The role of graphlets in viral processes on networks. J. Nonlinear Sci. 30 (2020), pp. 2309–2324.
- D. Mawhirter, B. Wu, D. Mehta, and C. Ai, ApproxG: Fast approximate parallel graphlet counting through accuracy control, 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), May 2018, pp. 533–542, Washington DC, USA.
- N. Pržulj, Biological network comparison using graphlet degree distribution. Bioinformatics. 23 (2007), pp. e177–e183.
- F. Safaei, F. Kashkooei Jahromi, and S. Fathi, A method for computing local contributions to graph energy based on Estrada–Benzi approach. Discrete Appl. Math. 260 (2019), pp. 214–226.
- B. Zhou, I. Gutman, J.A. de la Peña, J. Rada, and L. Mendoza, On the spectral moments and energy of graphs. MATCH Commun. Math. Comput. Chem 57 (2007), pp. 183–191.