• DOI:10.1103/PhysRevE.76.026113
• Corpus ID: 18973905

Uncovering the topology of configuration space networks.

@article{Gfeller2007UncoveringTT,
  title={Uncovering the topology of configuration space networks.},
  author={David Gfeller and David Morton de Lachapelle and Paolo De Los Rios and Guido Caldarelli and Francesco Rao},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  year={2007},
  volume={76 2 Pt 2},
  pages={
          026113
        }
}

• D. Gfeller, David Morton de Lachapelle, +2 authors F. Rao
• Published 20 April 2007
• Computer Science
• Physical review. E, Statistical, nonlinear, and soft matter physics

The configuration space network (CSN) of a dynamical system is an effective approach to represent the ensemble of configurations sampled during a simulation and their dynamic connectivity. To elucidate the connection between the CSN topology and the underlying free-energy landscape governing the system dynamics and thermodynamics, an analytical solution is provided to explain the heavy tail of the degree distribution, neighbor connectivity, and clustering coefficient. This derivation allows us to understand the universal CSN topology observed in systems ranging from a simple quadratic well to the native state of the beta3s peptide and a two-dimensional lattice heteropolymer. Moreover, CSNs are shown to fall in the general class of complex networks described by the fitness model.