Number of items: **3**.

## B

Braunstein, Lidia,

Wu, Zhenhua,

Chen, Yiping,

Buldyrev, Sergey,

Kalisky, Tomer,

Sreenivasan, Sameet,

López, Eduardo,

Cohen, Reuven,

Havlin, Shlomo and

Stanley, Eugene
(2007)

*Optimal Path and Minimal Spanning Trees in Random Weighted Networks.*
International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, 17 (7).
pp. 2215-2255.
Link to full text available through this repository.

- Abstract
We review results on the scaling of the optimal path length l(opt) in random networks with weighted links or nodes. We refer to such networks as "weighted" or "disordered" networks. The optimal path is the path with minimum sum of the weights. In strong disorder, where the maximal weight along the path dominates the sum, we find that l(opt) increases dramatically compared to the known small-world result for the minimum distance l(min) similar to log N, where N is the number of nodes. For Erdos-Renyi (ER) networks l(opt) similar to N-1/3, while for scale free (SF) networks, with degree distribution P(k) similar to k(-lambda), we find that l(opt) scales as N-(lambda 3)/(lambda/1) for 3 < lambda < 4 and as N-1/3 for 3 4. Thus, for these networks, the small-world nature is destroyed. For 2 < lambda < 3 in contrary, our numerical results suggest that l(opt) scales as ln(lambda-1) 1 N, representing still a small world. We also find numerically that for weak disorder l(opt) similar to ln N for ER models as well as for SF networks. We also review the transition between the strong and weak disorder regimes in the scaling properties of l(opt) for ER and SF networks and for a general distribution of weights tau, P(tau). For a weight distribution of the form P(tau) = 1/(a tau) with (tau(min) < tau < tau(max)) and a = ln tau(max)/ tmin, we find that there is a crossover network size N = N( a) at which the transition occurs. For N N the scaling behavior of l(opt) is in the strong disorder regime, while for N N the scaling behavior is in the weak disorder regime. The value of N can be determined from the expression l(infinity)(N) = ap(c), where l(infinity) is the optimal path length in the limit of strong disorder, A = ap(c) infinity and pc is the percolation threshold of the network. We suggest that for any P(tau) the distribution of optimal path lengths has a universal form which is controlled by the scaling parameter Z = l(infinity)/A where A = p(c)tau(c)/integral(tau c)(0) tau P(tau) d tau plays the role of the disorder strength and tau(c) is defined by integral(tau c)(0) P(tau)d tau = p(c). In case P(tau) similar to 1/(at), the equation for A is reduced to A = ap(c). The relation for A is derived analytically and supported by numerical simulations for Erdos-Renyi and scale-free graphs. We also determine which form of P(tau) can lead to strong disorder A infinity. We then study the minimum spanning tree (MST), which is the subset of links of the network connecting all nodes of the network such that it minimizes the sum of their weights. We show that the minimum spanning tree (MST) in the strong disorder limit is composed of percolation clusters, which we regard as "super-nodes", interconnected by a scale-free tree. The MST is also considered to be the skeleton of the network where the main transport occurs. We furthermore show that the MST can be partitioned into two distinct components, having significantly different transport properties, characterized by centrality - number of times a node (or link) is used by transport paths. One component the superhighways, for which the nodes (or links) with high centrality dominate, corresponds to the largest cluster at the percolation threshold (incipient infinite ercolation cluster) which is a subset of the MST. The other component, roads, includes the remaining nodes, low centrality nodes dominate. We find also that the distribution of the centrality for the incipient infinite percolation cluster satisfies a power law, with an exponent smaller than that for the entire MST. We demonstrate the significance identifying the superhighways by showing that one can improve significantly the global transport by improving a very small fraction of the network, the superhighways.

- Item type
- Article
- Subject(s)
- UNSPECIFIED
- Uncontrolled keywords
- Minimum spanning tree; Percolation; Scale-free; Optimization
- Centre
- CABDyN Complexity Centre

Buldyrev, Sergey,

Havlin, Shlomo,

López, Eduardo and

Stanley, Eugene
(2004)

*Universality of the optimal path in the strong disorder limit.*
Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 70 (3).
Link to full text available through this repository.

- Abstract
We study numerically the optimal paths in two and three dimensions on various disordered lattices in the limit of strong disorder. We find that the length l of the optimal path scales with geometric distance r , as l approximately r (d(opt) with d(opt) =1.22+/-0.01 for d=2 and 1.44+/-0.02 for d=3 , independent of whether the optimization is on a path of weighted bonds or sites, and independent of the lattice or its coordination number. Our finding suggests that the exponent d(opt) is universal, depending only on the dimension of the system.

- Item type
- Article
- Subject(s)
- UNSPECIFIED
- Uncontrolled keywords
- Universality; Optimization
- Centre
- CABDyN Complexity Centre

## C

Chen, Yiping,

López, Eduardo,

Havlin, Shlomo and

Stanley, Eugene
(2006)

*Universal behavior of optimal paths in weighted networks with general disorder.*
Physical Review Letters, 96 (6).
068702.
Link to full text available through this repository.

- Abstract
We study the statistics of the optimal path in both random and scale-free networks, where weights are taken from a general distribution P(w). We find that different types of disorder lead to the same universal behavior. Specifically, we find that a single parameter (S defined as AL(-1/v) for d-dimensional lattices, and S defined as AN(-1/3) for random networks) determines the distributions of the optimal path length, including both strong and weak disorder regimes. Here v is the percolation connectivity exponent, and A depends on the percolation threshold and P(w). We show that for a uniform P(w), Poisson or Gaussian, the crossover from weak to strong does not occur, and only weak disorder exists.

- Item type
- Article
- Subject(s)
- UNSPECIFIED
- Uncontrolled keywords
- Disordered Systems and Neutral Networks; Optimization; Statistical-physics
- Centre
- CABDyN Complexity Centre

This list was generated on **Fri Feb 23 10:21:16 2018 WET**.