We consider diffusion processes on power-law small-world networks in different dimensions. In one dimension, we find a rich phase diagram, with different transient and recurrent phases, including a critical line with continuously varying exponents. The results were obtained using self-consistent perturbation theory and can also be understood in terms of a scaling theory, which provides a general framework for understanding processes on small-world networks with different distributions of long-range links.PACS numbers: 89.75. Hc, 68.35.Ct, 05.40.Fb, 89.20.Ff Studying diffusion-related processes on networks, one can gain insight into synchronization [1] and spreading phenomena in natural, artificial, and social systems [2]. In this Letter, we consider a class of models on smallworld (SW) networks [3] with a power-law probability distribution of long-range links, where the probability of two nodes being connected goes as r −α , with some exponent α [4,5,6,7]. Such a structure can emerge in synchronization problems of distributed computing [8] where synchronization is achieved by introducing random communications between distant processors. Choosing a power-law SW network may be preferable, as it lowers the cost associated with communications. It was also recently argued [7] that "wiring-cost" considerations for spatially embedded networks, such as cortical networks [9] or on-chip logic networks [10], can generate such power-law-suppressed link-length distribution. A physical example of such a power-law SW network arises in diffusion on a randomly folded polymer discussed below [11,12].The addition of random long-range links to a regular d-dimensional network, producing a SW network, leads in many cases to a crossover to mean-field-like behavior [13], effectively becoming equivalent to averaging over the long-range links in an annealed fashion. Here we focus on systems where it is not the case, and the contrast between the quenched and annealed systems is strong [5,14]. We develop a perturbative method for the quenched network, which is asymptotically exact, as confirmed by numerics.Varying the exponent α, controlling the distribution of length of the links, the diffusive dynamics gives rise to a rich phase diagram, as the connection topology interpolates between the original "plain" SW (α=0) and the purely short-range connected (α=∞) network. The phase diagram can also be understood in terms of simple scaling ideas, showing that the breakdown of mean-field theory is associated with the relevance of the operator for scattering off a single link.Diffusion-Related Processes-We start with a ddimensional lattice of linear size L and add long-range links, connecting two sites with a probability proportional to pr −α , where r is the distance between the two sites, p is the probability of a site to have a random link, and α is the exponent of the decay, ranging from 0 to ∞. Although the original problem can have only one random link between two distant sites, the analytics are slightly simpler if pairs of sites may be ...