Analysis of Large Graphs
Analysis of these graphs requires introduction of new parameters and methods conceptually different than the ones used for relatively smaller graphs. We describe new parameters and methods for the analysis of these graphs and also describe various models to represent them in this chapter. Two widely used models for the large graphs representing real networks are small-world and scale-free models. The former means the average distance between any two nodes in large graphs is small and only few nodes with high degrees exist with majority of the nodes having low degrees in the latter.
- 2.Batagelj V, Zaversnik M (2003) An O(m) algorithm for cores decomposition of networks. CoRR (Computing research repository), arXiv:0310049
- 3.Blaar H, Karnstedt M, Lange T, Winter R (2005) Possibilities to solve the clique problem by thread parallelism using task pools. In: Proceedings of the 19th IEEE international parallel and distributed processing symposium (IPDPS05)Workshop 5 Volume 06 in GermanyGoogle Scholar
- 6.Erciyes K (2015) Distributed and sequential algorithms for bioinformatics. Springer, Berlin (chapters 10–11)Google Scholar
- 10.Jaber K, Rashid NA, Abdullah R (2009) The parallel maximal cliques algorithm for protein sequence clustering. Am J Appl Sci 6:13681372Google Scholar
- 16.Perron O (1907) Mathematische Annalen. Zur Theorie der Matrices 64(2):248–263Google Scholar