Random walks in directed modular networks
arXiv:1202.4047 · doi:10.1088/1742-5468/2014/12/P12003
Abstract
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 Erdős-Rényi and Barabási-Albert models. The results are illustrated with respect to the macaque cortical network, providing subsidies for improving transportation and communication systems.
References in corpus (9)
- Community structure in directed networks
- Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
- Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems
- Origins of power-law degree distribution in the heterogeneity of human activity in social networks
- Localization of maximal entropy random walk
- Spectral coarse-graining of complex networks
- Laplacian spectra of complex networks and random walks on them: Are scale-free architectures really important?
- Scaling of degree correlations and the influence on diffusion in scale-free networks
- Random Walks on Complex Networks