Defining least community as a homogeneous group in complex networks

Defining Least Community as a Homogeneous Group in Complex Networks

This paper introduces a new concept of least community that is as homogeneous as a random graph, and develops a new community detection algorithm from the perspective of homogeneity or heterogeneity. Based on this concept, we adopt head/tail breaks – a newly developed classification scheme for data with a heavy-tailed distribution – and rely on edge betweenness given its heavy-tailed distributi...

Defining and Detecting Emergence in Complex Networks

Emergence is seen as the most significant feature discriminating “complex” from “non complex” systems. Nevertheless, no standard definition of emergence is currently available in the literature. This lack of a shared view affects the development of tools to detect and model emergence for both scientific and engineering applications. Here we review some definitions of emergence with the aim to d...

Defining nodes in complex brain networks

Network science holds great promise for expanding our understanding of the human brain in health, disease, development, and aging. Network analyses are quickly becoming the method of choice for analyzing functional MRI data. However, many technical issues have yet to be confronted in order to optimize results. One particular issue that remains controversial in functional brain network analyses ...

The Automorphism Group of a Homogeneous Almost Complex Manifold (*)

Traffic Driven Epidemic Spreading in Homogeneous Networks with Community Structure

To understand the effect of the community structure on the epidemic propagation in homogeneous networks, a model of pseudo-random network is presented with adjustable community structure and constant average degree. In the scenario that the propagation is driven by reaction processes from nodes to all neighbors, pronounced community structure can reduce the epidemic propagation velocity. While ...