Modeling Connectivity in Terms of Network Activity

Abstract

A new complex network model is proposed which is founded on growth with new connections being established proportionally to the current dynamical activity of each node, which can be understood as a generalization of the Barabasi-Albert static model. By using several topological measurements, as well as optimal multivariate methods (canonical analysis and maximum likelihood decision), we show that this new model provides, among several other theoretical types of networks including Watts-Strogatz small-world networks, the greatest compatibility with three real-world cortical networks.

A working manuscript, 5 pages, 3 figures, 1 table

References in corpus (4)

• Characterization of complex networks: A survey of measurements
• Complex cooperative networks from evolutionary preferential attachment
• Correlations between structure and dynamics in complex networks
• Network synchronization: Optimal and Pessimal Scale-Free Topologies

Cited by in corpus (1)

• Evolving Networks and the Development of Neural Systems