arXiv subjects

José S. Andrade, Jr.

Publications and source records attributed to José S. Andrade, Jr..

4 recordsLinked to original sources

arXiv2021

Fast algorithm to identify cluster synchrony through fibration symmetries in large information-processing networks

Recent studies revealed an important interplay between the detailed structure of fibration symmetric circuits and the functionality of biological and non-biological networks within which they have be identified. The presence of these circuits in complex networks are directed related to the phenomenon of cluster synchronization, which produces patterns of synchronized group of nodes. Here we present a fast, and memory efficient, algorithm to identify fibration symmetries over information-processing networks. This algorithm is specially suitable for large and sparse networks since it has runtime of complexity $O(M\log N)$ and requires $O(M+N)$ of memory resources, where $N$ and $M$ are the number of nodes and edges in the network, respectively. We propose a modification on the so-called refinement paradigm to identify circuits symmetrical to information flow (i.e., fibers) by finding the coarsest refinement partition over the network. Finally, we show that the presented algorithm provides an optimal procedure for identifying fibers, overcoming the current approaches used in the literature.

q-bio.MN↗

arXiv2021

Superspreading k-cores at the center of COVID-19 pandemic persistence

The spread of COVID-19 caused by the recently discovered SARS-CoV-2 virus has become a worldwide problem with devastating consequences. To slow down the spread of the pandemic, mass quarantines have been implemented globally, provoking further social and economic disruptions. Here, we implement a comprehensive contact tracing network analysis to find an optimized quarantine protocol to dismantle the chain of transmission of coronavirus with minimal disruptions to society. We track billions of anonymized GPS human mobility datapoints from a compilation of hundreds of mobile apps deployed in Latin America to monitor the evolution of the contact network of disease transmission before and after the confinements. As a consequence of the lockdowns, people's mobility across the region decreases by $\sim$53\%, which results in a drastic disintegration of the transmission network by $\sim$90\%. However, this disintegration did not halt the spreading of the disease. Our analysis indicates that superspreading k-core structures persist in the transmission network to prolong the pandemic. Once the k-cores are identified, an optimized strategy to break the chain of transmission is to quarantine a minimal number of 'weak links' with high betweenness centrality connecting the large k-cores. As countries built contact tracing apps to fight the pandemic, our results could turn into a valuable resource to help deploy quarantine protocols with minimized disruptions.

physics.soc-ph↗

arXiv2009

Biased Percolation on Scale-free Networks

Biased (degree-dependent) percolation was recently shown to provide new strategies for turning robust networks fragile and vice versa. Here we present more detailed results for biased edge percolation on scale-free networks. We assume a network in which the probability for an edge between nodes $i$ and $j$ to be retained is proportional to $(k_ik_j)^{-α}$ with $k_i$ and $k_j$ the degrees of the nodes. We discuss two methods of network reconstruction, sequential and simultaneous, and investigate their properties by analytical and numerical means. The system is examined away from the percolation transition, where the size of the giant cluster is obtained, and close to the transition, where nonuniversal critical exponents are extracted using the generating functions method. The theory is found to agree quite well with simulations. By introducing an extension of the Fortuin-Kasteleyn construction, we find that biased percolation is well described by the $q\to 1$ limit of the $q$-state Potts model with inhomogeneous couplings.

cond-mat.stat-mech↗

arXiv2000

Flow Between Two Sites on a Percolation Cluster

We study the flow of fluid in porous media in dimensions $d=2$ and 3. The medium is modeled by bond percolation on a lattice of $L^d$ sites, while the flow front is modeled by tracer particles driven by a pressure difference between two fixed sites (``wells'') separated by Euclidean distance $r$. We investigate the distribution function of the shortest path connecting the two sites, and propose a scaling {\it Ansatz} that accounts for the dependence of this distribution (i) on the size of the system, $L$, and (ii) on the bond occupancy probability, $p$. We confirm by extensive simulations that the {\it Ansatz} holds for $d=2$ and 3, and calculate the relevant scaling parameters. We also study two dynamical quantities: the minimal traveling time of a tracer particle between the wells and the length of the path corresponding to the minimal traveling time ``fastest path'', which is not identical to the shortest path. A scaling {\it Ansatz} for these dynamical quantities also includes the effect of finite system size $L$ and off-critical bond occupation probability $p$. We find that the scaling form for the distribution functions for these dynamical quantities for $d=2$ and 3 is similar to that for the shortest path but with different critical exponents. The scaling form is represented as the product of a power law and three exponential cutoff functions. We summarize our results in a table which contains estimates for all parameters which characterize the scaling form for the shortest path and the minimal traveling time in 2 and 3 dimensions; these parameters are the fractal dimension, the power law exponent, and the constants and exponents that characterize the exponential cutoff functions.

cond-mat.stat-mech↗