arXiv subjects

Luciano da Fontoura Costa

Publications and source records attributed to Luciano da Fontoura Costa.

At least 73 records · Page 4Linked to original sources

arXiv2007

On the effects of geographical constraints on task execution in complex networks

In the present work we investigate the effects of spatial constraints on the efficiency of task execution in systems underlain by geographical complex networks where the probability of connection decreases with the distance between the nodes. The investigation considers several configurations of the parameters defining the network connectivity, and the Barabasi-Albert network model is also considered for comparisons. The results show that the effect of connectivity is significant only for shorter tasks, that the locality of connections implied by the spatial constraints reduces efficency, and that the addition of edges can improve the efficiency of the execution, although with increasing locality of the connections the improvement is small.

physics.soc-ph↗

arXiv2007

Concentric Characterization and Classification of Complex Network Nodes: Theory and Application to Institutional Collaboration

Differently from theoretical scale-free networks, most of real networks present multi-scale behavior with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by Concentric (or Hierarchical) Measurements. In this paper we explore the possibility of using a set of Concentric Measurements and agglomerative clustering methods in order to obtain a set of functional groups of nodes. Concentric clustering coefficient and convergence ratio are chosen as segregation parameters for the analysis of a institutional collaboration network including various known communities (departments of the University of São Paulo). A dendogram is obtained and the results are analyzed and discussed. Among the interesting obtained findings, we emphasize the scale-free nature of the obtained network, as well as the identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along the concentric levels, contrariwise to the non-uniform pattern found in theoretical scale free networks such as the BA model.

physics.soc-ph↗

arXiv2007

Analyzing Trails in Complex Networks

Even more interesting than the intricate organization of complex networks are the dynamical behavior of systems which such structures underly. Among the many types of dynamics, one particularly interesting category involves the evolution of trails left by moving agents progressing through random walks and dilating processes in a complex network. The emergence of trails is present in many dynamical process, such as pedestrian traffic, information flow and metabolic pathways. Important problems related with trails include the reconstruction of the trail and the identification of its source, when complete knowledge of the trail is missing. In addition, the following of trails in multi-agent systems represent a particularly interesting situation related to pedestrian dynamics and swarming intelligence. The present work addresses these three issues while taking into account permanent and transient marks left in the visited nodes. Different topologies are considered for trail reconstruction and trail source identification, including four complex networks models and four real networks, namely the Internet, the US airlines network, an email network and the scientific collaboration network of complex network researchers. Our results show that the topology of the network influence in trail reconstruction, source identification and agent dynamics.

physics.comp-ph↗

arXiv2007

Multiple complex networks emerging from individual interactions

Systems composed of distinct complex networks are present in many real-world environments, from society to ecological systems. In the present paper, we propose a network model obtained as a consequence of interactions between two species (e.g. predator and prey). Fields are produced and sensed by the individuals, defining spatio-temporal patterns which are strongly affected by the attraction intensity between individuals from the same species. The dynamical evolution of the system, including the change of individuals between different clusters, is investigated by building two complex networks having the individuals as nodes. In the first network, the edge weight is given by the Euclidean distance between every two individuals and, in the case of the second network, by the amount of time two individuals stay close one another. A third network is obtained from the two previous networks whose nodes correspond to the spatially congruent groups. The system evolves to an organized state where Gaussian and scale-free-like strength distributions emerge, respectively, in the predator and prey networks. Such a different connectivity is mainly a consequence of preys elimination. Some configurations favor the survival of preys or higher efficiency of predator activity.

physics.bio-ph↗

arXiv2007

Visual Saliency and Attention as Random Walks on Complex Networks

The current article shows how concepts from the areas of random walks, Markov chains, complex networks and image analysis can be naturally combined in order to provide a unified and biologically plausible model relating saliency and visual attention. Two types of models are proposed: (i) images are converted into complex networks by considering pixels as nodes while connections are established in terms of fields of influence defined by visual features such as tangent fields induced by gray-level contrasts and distance; and (ii) image pixels exhibiting particularly distinctive values of visual properties such as gray-level intensity, contrast, size of objects, orientation and texture are mapped into nodes and the weights of links are defined in order to favor transitions between regions with similar or different visual features, also taking the distance between the nodes into account. Preferential random walks are performed on such networks in order to emulate attentional shifts and eye movements, and the saliency of each region is obtained in terms of the frequency of visits to each node at equilibrium. In the case of the first model, there is a definite tendency to emphasize not only high curvature points but also convergences of the tangent field. The frequency of visits is found to be strongly correlated with the node degrees (strengths) for this model. Different results have been obtained for the second model as a consequence of the directed and asymmetric nature of the respectively obtained networks.

physics.med-ph↗

arXiv2007

Morphological relationship between axon and dendritic arborizations as revealed by Minkowski functionals

The spatial structure of the axonal and dendritic arborizations is closely related to the functionality of specific neurons or neuronal subsystems. The present work describes how multiscale Minkowski functionals can be used in order to characterize and compare the spatial organization of these two types of arborizations. The discrimination potential of the method is illustrated with respect to three classes of cortical neurons.

q-bio.QM↗

arXiv2007

Effective networks for real-time distributed processing

The problem of real-time processing is one of the most challenging current issues in computer sciences. Because of the large amount of data to be treated in a limited period of time, parallel and distributed systems are required, whose performance depends on a series of factors including the interconnectivity of the processing elements, the application model and the communication protocol. Given their flexibility for representing and modeling natural and human-made systems (such as the Internet and WWW), complex networks have become a primary choice in many research areas. The current work presents how the concepts and methods of complex networks can be used to develop realistic models and simulations of distributed real-time system while taking into account two representative interconnection models: uniformly random and scale free (Barabasi-Albert), including the presence of background traffic of messages. The interesting obtained results include the identification of the uniformly random interconnectivity scheme as being largely more efficient than the scale-free counterpart.

physics.soc-ph↗

arXiv2007

The rich-club phenomenon across complex network hierarchies

The so-called rich-club phenomenon in a complex network is characterized when nodes of higher degree (hubs) are better connected among themselves than are nodes with smaller degree. The presence of the rich-club phenomenon may be an indicator of several interesting high-level network properties, such as tolerance to hub failures. Here we investigate the existence of the rich-club phenomenon across the hierarchical degrees of a number of real-world networks. Our simulations reveal that the phenomenon may appear in some hierarchies but not in others and, moreover, that it may appear and disappear as we move across hierarchies. This reveals the interesting possibility of non-monotonic behavior of the phenomenon; the possible implications of our findings are discussed.

physics.soc-ph↗

arXiv2007

Comparative analysis of protein structure using multiscale additive functionals

This work reports a new methodology aimed at describing characteristics of protein structural shapes, and suggests a framework in which to resolve or classify automatically such structures into known families. This new approach to protein structure characterization is based on elements of integral geometry using biologically relevant measurements of shape and considering them on a multi-scale representation which align the proposed methodology to the recently reported "tube picture" of a protein structure as a minimal representation model. The method has been applied with good results to a subset of protein structures known to be especially challenging to revert into families, confirming the potential of the proposed method for accurate structure classification.

q-bio.QM↗

arXiv2007

On the Separability of Attractors in Grandmother Dynamic Systems with Structured Connectivity

The combination of complex networks and dynamic systems research is poised to yield some of the most interesting theoretic and applied scientific results along the forthcoming decades. The present work addresses a particularly important related aspect, namely the quantification of how well separated can the attractors be in dynamic systems underlined by four types of complex networks (Erdős-Rényi, Barabási-Albert, Watts-Strogatz and as well as a geographic model). Attention is focused on grandmother dynamic systems, where each state variable (associated to each node) is used to represent a specific prototype pattern (attractor). By assuming that the attractors spread their influence among its neighboring nodes through a diffusive process, it is possible to overlook the specific details of specific dynamics and focus attention on the separability among such attractors. This property is defined in terms of two separation indices (one individual to each prototype and the other considering also the immediate neighborhood) reflecting the balance and proximity to attractors revealed by the activation of the network after a diffusive process. The separation index considering also the neighborhood was found to be much more informative, while the best separability was observed for the Watts-Strogatz and the geographic models. The effects of the involved parameters on the separability were investigated by correlation and path analyses. The obtained results suggest the special importance of some measurements in underlying the relationship between topology and dynamics.

physics.data-an↗

arXiv2006

Identifying Complex Networks by Random Walks

The possibility to identify the nature (e.g. random or scale free) of complex networks while performing respective random walks is investigated with respect to autonomous agents based on Bayesian decision theory and humans navigating through a graphic-interactive interface. The results indicate that the type of the network (choice between random and scale free models) can be correctly estimated in most cases.

physics.comp-ph↗

arXiv2006

2D pattern evolution constrained by complex network dynamics

Complex networks have established themselves along the last years as being particularly suitable and flexible for representing and modeling several complex natural and human-made systems. At the same time in which the structural intricacies of such networks are being revealed and understood, efforts have also been directed at investigating how such connectivity properties define and constrain the dynamics of systems unfolding on such structures. However, lesser attention has been focused on hybrid systems, \textit{i.e.} involving more than one type of network and/or dynamics. Because several real systems present such an organization (\textit{e.g.} the dynamics of a disease coexisting with the dynamics of the immune system), it becomes important to address such hybrid systems. The current paper investigates a specific system involving a diffusive (linear and non-linear) dynamics taking place in a regular network while interacting with a complex network of defensive agents following Erdös-Rényi and Barabási-Albert graph models, whose nodes can be displaced spatially. More specifically, the complex network is expected to control, and if possible to extinguish, the diffusion of some given unwanted process (\textit{e.g.} fire, oil spilling, pest dissemination, and virus or bacteria reproduction during an infection). Two types of pattern evolution are considered: Fick and Gray-Scott. The nodes of the defensive network then interact with the diffusing patterns and communicate between themselves in order to control the spreading. The main findings include the identification of higher efficiency for the Barabási-Albert control networks.

physics.comp-ph↗

arXiv2006

On the Dynamics of the $h-$index in Complex Networks with Coexisting Communities

This article investigates the evolution of the $h-$index in a complex network including two communities (in the sense of having different features) with the same number of authors whose yearly productions follow the Zipf's law. Models considering indiscriminate citations, as well as citations preferential to the fitness values of each community and/or the number of existing citations are proposed and numerically simulated. The $h-$indices of each type of author is estimated along a period of 20 years, while the number of authors remains constant. Interesting results are obtained including the fact that, for the model where citations are preferential to both community fitness and number of existing citations per article, the $h-$indices of the community with the largest fitness value are only moderately increased while the indices of the other community are severely and irreversibly limited to low values. Three possible strategies are discussed in order to change this situation. In addition, based on such findings, a new version of the $h-$index is proposed involving the automated identification of virtual citations which can provide complementary and unbiased quantification of the relevance of scientific works.

physics.soc-ph↗

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

Complex Networks: New Concepts and Tools for Real-Time Imaging and Vision

This article discusses how concepts and methods of complex networks can be applied to real-time imaging and computer vision. After a brief introduction of complex networks basic concepts, their use as means to represent and characterize images, as well as for modeling visual saliency, are briefly described. The possibility to apply complex networks in order to model and simulate the performance of parallel and distributed computing systems for performance of visual methods is also proposed.

cs.CV↗

arXiv2006

Learning about knowledge: A complex network approach

This article describes an approach to modeling knowledge acquisition in terms of walks along complex networks. Each subset of knowledge is represented as a node, and relations between such knowledge are expressed as edges. Two types of edges are considered, corresponding to free and conditional transitions. The latter case implies that a node can only be reached after visiting previously a set of nodes (the required conditions). The process of knowledge acquisition can then be simulated by considering the number of nodes visited as a single agent moves along the network, starting from its lowest layer. It is shown that hierarchical networks, i.e. networks composed of successive interconnected layers, arise naturally as a consequence of compositions of the prerequisite relationships between the nodes. In order to avoid deadlocks, i.e. unreachable nodes, the subnetwork in each layer is assumed to be a connected component. Several configurations of such hierarchical knowledge networks are simulated and the performance of the moving agent quantified in terms of the percentage of visited nodes after each movement. The Barabási-Albert and random models are considered for the layer and interconnecting subnetworks. Although all subnetworks in each realization have the same number of nodes, several interconnectivities, defined by the average node degree of the interconnection networks, have been considered. Two visiting strategies are investigated: random choice among the existing edges and preferential choice to so far untracked edges. A series of interesting results are obtained, including the identification of a series of plateaux of knowledge stagnation in the case of the preferential movements strategy in presence of conditional edges.

physics.soc-ph↗

arXiv2006

A Fast and Accurate Nonlinear Spectral Method for Image Recognition and Registration

This article addresses the problem of two- and higher dimensional pattern matching, i.e. the identification of instances of a template within a larger signal space, which is a form of registration. Unlike traditional correlation, we aim at obtaining more selective matchings by considering more strict comparisons of gray-level intensity. In order to achieve fast matching, a nonlinear thresholded version of the fast Fourier transform is applied to a gray-level decomposition of the original 2D image. The potential of the method is substantiated with respect to real data involving the selective identification of neuronal cell bodies in gray-level images.

cs.DC↗

arXiv2006

Exploring Complex Networks through Random Walks

Most real complex networks -- such as protein interactions, social contacts, the internet -- are only partially known and available to us. While the process of exploring such networks in many cases resembles a random walk, it becomes a key issue to investigate and characterize how effectively the nodes and edges of such networks can be covered by different strategies. At the same time, it is critically important to infer how well can topological measurements such as the average node degree and average clustering coefficient be estimated during such network explorations. The present article addresses these problems by considering random and Barabási-Albert (BA) network models with varying connectivity explored by three types of random walks: traditional, preferential to untracked edges, and preferential to unvisited nodes. A series of relevant results are obtained, including the fact that random and BA models with the same size and average node degree allow similar node and edge coverage efficiency, the identification of linear scaling with the size of the network of the random walk step at which a given percentage of the nodes/edges is covered, and the critical result that the estimation of the averaged node degree and clustering coefficient by random walks on BA networks often leads to heavily biased results. Many are the theoretical and practical implications of such results.

physics.soc-ph↗