  Complex networks exhibit a wide range of collective dynamic phenomena,
including synchronization, diffusion, relaxation, and coordination processes.
Their asymptotic dynamics is generically characterized by the local Jacobian,
graph Laplacian or a similar linear operator. The structure of networks with
regular, small-world and random connectivities are reasonably well understood,
but their collective dynamical properties remain largely unknown. Here we
present a two-stage mean-field theory to derive analytic expressions for
network spectra. A single formula covers the spectrum from regular via
small-world to strongly randomized topologies in Watts-Strogatz networks,
explaining the simultaneous dependencies on network size N, average degree k
and topological randomness q. We present simplified analytic predictions for
the second largest and smallest eigenvalue, and numerical checks confirm our
theoretical predictions for zero, small and moderate topological randomness q,
including the entire small-world regime. For large q of the order of one, we
apply standard random matrix theory thereby overarching the full range from
regular to randomized network topologies. These results may contribute to our
analytic and mechanistic understanding of collective relaxation phenomena of
network dynamical systems.
