arXiv subjects

Gonzalo Travieso

Publications and source records attributed to Gonzalo Travieso.

25 records · Page 2Linked to original sources

arXiv2006

Protein domain connectivity and essentiality

Protein-protein interactions can be properly modeled as scale-free complex networks, while the lethality of proteins has been correlated with the node degrees, therefore defining a lethality-centrality rule. In this work we revisit this relevant problem by focusing attention not on proteins as a whole, but on their functional domains, which are ultimately responsible for their binding potential. Four networks are considered: the original protein-protein interaction network, its randomized version, and two domain networks assuming different lethality hypotheses. By using formal statistical analysis, we show that the correlation between connectivity and essentiality is higher for domains than for proteins.

cond-mat.dis-nn↗

arXiv2006

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.

cond-mat.dis-nn↗

arXiv2006

The Spread of Opinions and Proportional Voting

Election results are determined by numerous social factors that affect the formation of opinion of the voters, including the network of interactions between them and the dynamics of opinion influence. In this work we study the result of proportional elections using an opinion dynamics model similar to simple opinion spreading over a complex network. Erdos-Renyi, Barabasi-Albert, regular lattices and randomly augmented lattices are considered as models of the underlying social networks. The model reproduces the power law behavior of number of candidates with a given number of votes found in real elections with the correct slope, a cutoff for larger number of votes and a plateau for small number of votes. It is found that the small world property of the underlying network is fundamental for the emergence of the power law regime.

physics.soc-ph↗

arXiv2006

Fast Community Identification by Hierarchical Growth

A new method for community identification is proposed which is founded on the analysis of successive neighborhoods, reached through hierarchical growth from a starting vertex, and on the definition of communities as a subgraph whose number of inner connections is larger than outer connections. In order to determine the precision and speed of the method, it is compared with one of the most popular community identification approaches, namely Girvan and Newman's algorithm. Although the hierarchical growth method is not as precise as Girvan and Newman's method, it is potentially faster than most community finding algorithms.

physics.soc-ph↗

arXiv2005

Complex Grid Computing

This article investigates the performance of grid computing systems whose interconnections are given by random and scale-free complex network models. Regular networks, which are common in parallel computing architectures, are also used as a standard for comparison. The processing load is assigned to the processing nodes on demand, and the efficiency of the overall computing is quantified in terms of the respective speed-ups. It is found that random networks allow higher computing efficiency than their scale-free counterparts as a consequence of the smaller number of isolated clusters implied by the former model. At the same time, for fixed cluster sizes, the scale free model tend to provide slightly better efficiency. Two modifications of the random and scale free paradigms, where new connections tend to favor more recently added nodes, are proposed and shown to be more effective for grid computing than the standard models. A well-defined correlation is observed between the topological properties of the network and their respective computing efficiency.

cond-mat.stat-mech↗

arXiv2005

Strength Distribution in Derivative Networks

This article describes a complex network model whose weights are proportional to the difference between uniformly distributed ``fitness'' values assigned to the nodes. It is shown both analytically and experimentally that the strength density (i.e. the weighted node degree) for this model, called derivative complex networks, follows a power law with exponent $γ<1$ if the fitness has an upper limit and $γ>1$ if the fitness has no upper limit but a positive lower limit. Possible implications for neuronal networks topology and dynamics are also discussed.

cond-mat.stat-mech↗

arXiv2003

Parallel implementation of a lattice-gauge-theory code: studying quark confinement on PC clusters

We consider the implementation of a parallel Monte Carlo code for high-performance simulations on PC clusters with MPI. We carry out tests of speedup and efficiency. The code is used for numerical simulations of pure SU(2) lattice gauge theory at very large lattice volumes, in order to study the infrared behavior of gluon and ghost propagators. This problem is directly related to the confinement of quarks and gluons in the physics of strong interactions.

hep-lat↗