Higher order assortativity in complex networks
نویسندگان
چکیده
منابع مشابه
Higher order assortativity in complex networks
Assortativity was first introduced by Newman and has been extensively studied and applied to many real world networked systems since then. Assortativity is a graph metrics and describes the tendency of high degree nodes to be directly connected to high degree nodes and low degree nodes to low degree nodes. It can be interpreted as a first order measure of the connection between nodes, i.e. the ...
متن کاملClassifying Complex Networks using Unbiased Local Assortativity
Assortativity is a network-level measure which quantifies the tendency of nodes to mix with similar nodes in a network. Local assortativity has been introduced as a measure to analyse the contribution of individual nodes to network assortativity. In this paper we argue that there is a bias in the formulation of local assortativity which favours low-degree nodes. We show that, after the bias is ...
متن کاملHigher-order organization of complex networks
Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks--at the level of small network subgraphs--remains large...
متن کاملDegree distribution and assortativity in line graphs of complex networks
Topological characteristics of links of complex networks influence the dynamical processes executed on networks triggered by links, such as cascading failures triggered by links in power grids and epidemic spread due to link infection. The line graph transforms links in the original graph into nodes. In this paper, we investigate how graph metrics in the original graph are mapped into those for...
متن کاملRevising the simple measures of assortativity in complex networks.
We find that traditional statistics for measuring degree mixing are strongly affected by superrich nodes. To counteract and measure the effect of superrich nodes, we propose a paradigm to quantify the mixing pattern of a real network in which different mixing patterns may appear among low-degree nodes and among high-degree nodes. This paradigm and the simple revised measure uncover the true com...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: European Journal of Operational Research
سال: 2017
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2017.04.028