One natural way to model human networks, such as social networks, transportation networks, or the internet, is with random graphs.
This paper summarizes the foundations of random graph theory, developed by Paul Erd¨os and Alfred R´enyi in 1958, and some common
techniques used to analyze random graphs. Three more generalized
random graph models are also explored: the configuration model, the
small-world model, and the preferential attachment model. The similarity of these models to human networks is evaluated based on four
criteria: average path length, degree distribution, clustering coefficient, and static or dynamic nature of the graph.
Applications are open for the ACT Applied Category Theory Research School 2018! And because arithmetic science and geometric science are connected, and support one another, the full knowledge of numbers cannot be presented without encountering some geometry, or without seeing that operating in this way on numbers is close to geometry; the method is full…
S. Janson. (2010)cite arxiv:1009.2376Comment: 72 pages. This version contains a new section on pure graphons and some other minor additions, including a new appendix and new references.
J. Zheng, S. Pawar, and D. Goodman. (2017)cite arxiv:1710.04626Comment: Submitted to IEEE Transactions on Visualization and Computer Graphics on 11/04/2018.
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