Damage Spreading and Criticality in Finite Random Dynamical Networks

Physics – Condensed Matter – Disordered Systems and Neural Networks

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

4 pages, 4 eps figures

Scientific paper

10.1103/PhysRevLett.99.248701

We systematically study and compare damage spreading at the sparse percolation (SP) limit for random boolean and threshold networks with perturbations that are independent of the network size $N$. This limit is relevant to information and damage propagation in many technological and natural networks. Using finite size scaling, we identify a new characteristic connectivity $K_s$, at which the average number of damaged nodes $\bar d$, after a large number of dynamical updates, is independent of $N$. Based on marginal damage spreading, we determine the critical connectivity $K_c^{sparse}(N)$ for finite $N$ at the SP limit and show that it systematically deviates from $K_c$, established by the annealed approximation, even for large system sizes. Our findings can potentially explain the results recently obtained for gene regulatory networks and have important implications for the evolution of dynamical networks that solve specific computational or functional tasks.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Damage Spreading and Criticality in Finite Random Dynamical Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Damage Spreading and Criticality in Finite Random Dynamical Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Damage Spreading and Criticality in Finite Random Dynamical Networks will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-603280

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.