39 research outputs found
Topology and Dynamics in Complex Networks: The Role of Edge Reciprocity
A key issue in complex systems regards the relationship between topology and
dynamics. In this work, we use a recently introduced network property known as
steering coefficient as a means to approach this issue with respect to
different directed complex network systems under varying dynamics. Theoretical
and real-world networks are considered, and the influences of reciprocity and
average degree on the steering coefficient are quantified. A number of
interesting results are reported that can assist the design of complex systems
exhibiting larger or smaller relationships between topology and dynamics
Random walks in directed modular networks
Because diffusion typically involves symmetric interactions, scant attention
has been focused on studying asymmetric cases. However, important networked
systems underlain by diffusion (e.g. cortical networks and WWW) are inherently
directed. In the case of undirected diffusion, it can be shown that the
steady-state probability of the random walk dynamics is fully correlated with
the degree, which no longer holds for directed networks. We investigate the
relationship between such probability and the inward node degree, which we call
efficiency, in modular networks. Our findings show that the efficiency of a
given community depends mostly on the balance between its ingoing and outgoing
connections. In addition, we derive analytical expressions to show that the
internal degree of the nodes do not play a crucial role in their efficiency,
when considering the Erd\H{o}s-R\'enyi and Barab\'asi-Albert models. The
results are illustrated with respect to the macaque cortical network, providing
subsidies for improving transportation and communication systems
A framework for evaluating complex networks measurements
A good deal of current research in complex networks involves the
characterization and/or classification of the topological properties of given
structures, which has motivated several respective measurements. This letter
proposes a framework for evaluating the quality of complex network measurements
in terms of their effective resolution, degree of degeneracy and
discriminability. The potential of the suggested approach is illustrated with
respect to comparing the characterization of several model and real-world
networks by using concentric and symmetry measurements. The results indicate a
markedly superior performance for the latter type of mapping