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.Comment: 3 page

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 Barab\'asi-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.Comment: 5 pages, 5 figure

The Path-Star Transformation and its Effects on Complex Networks

A good deal of the connectivity of complex networks can be characterized in terms of their constituent paths and hubs. For instance, the Barab\'asi-Albert model is known to incorporate a significative number of hubs and relatively short paths. On the other hand, the Watts-Strogatz model is underlain by a long path and almost complete absence of hubs. The present work investigates how the topology of complex networks changes when a path is transformed into a star (or, for long paths, a hub). Such a transformation keeps the number of nodes and does not increase the number of edges in the network, but has potential for greatly changing the network topology. Several interesting results are reported with respect to Erdos-R\'enyi, Barab\'asi-Albert and Watts-Strogats models, including the unexpected finding that the diameter and average shortest path length of the former type of networks are little affected by the path-star transformation. In addition to providing insight about the organization of complex networks, such transformations are also potentially useful for improving specific aspects of the network connectivity, e.g. average shortest path length as required for expedite communication between nodes.Comment: 8 pages, 2 figures, 1 table. A working manuscript, comments welcome

Diffusion of Time-Varying Signals in Complex Networks: A Structure-Dynamics Investigation Focusing the Distance to the Source of Activation

The way in which different types of dynamics unfold in complex networks is intrinsically related to the propagation of activation along nodes, which is strongly affected by the network connectivity. In this work we investigate to which extent a time-varying signal emanating from a specific node is modified as it diffuses, at the equilibrium regime, along uniformly random (Erd\H{o}s-R\'enyi) and scale-free (Barab\'asi-Albert) networks. The degree of preservation is quantified in terms of the Pearson cross-correlation between the original signal and the derivative of the signals appearing at each node along time. Several interesting results are reported. First, the fact that quite distinct signals are typically obtained at different nodes in the considered networks implies mean-field approaches to be completely inadequate. It has also been found that the peak and lag of the similarity time-signatures obtained for each specific node are strongly related to the respective distance between that node and the source node. Such a relationship tends to decrease with the average degree of the networks. Also, in the case of the lag, a less intense relationship is verified for scale-free networks. No relationship was found between the dispersion of the similarity signature and the distance to the source.Comment: 9 pages, 5 figure

An Early Modeling Approach to Digital Electronics

An Early modeling approach of transistors characterized by simplicity and accuracy in representing intrinsic non-linearities is applied to the characterization of propagation delay and level transition switching properties of NPN and PNP small signal transistors. Eight types of devices were considered, each represented by 5 samples taken from the same lot, totaling 20 NPN and 20 PNP transistors. Four switching time measurements were experimentally obtained, and the transistors also had their Early parameters $V_a$ (the Early voltage) and $s$ (a proportionality constant such that $R_o = 1/tan(s I_B)$ accurately estimated by using an experimental-numeric procedure that involves Hough transform accumulation in order to identify the crossing of the base current ($I_B$) indexed characteristic isolines, yielding the respective $V_a$. The timing measurements exhibited strong positive Pearson correlations when taken pairwise. When these measurements were compared individually to the respective Early parameters, no significant Pearson correlation was obtained. However, a strong relationship was observed between the product of the two Early parameters and the ratio between the fall and rise time. A Pearson correlation coefficient of 0.78 was observed between these variables in the case of NPN devices. This suggests that transistors with larger average current gain tend to have more similar rise and fall times. The different relationship observed for PNP devices (Pearson 0.41) suggests some intrinsic difference in the way the Early parameters influence the rise and fall times of small signal transistors.Comment: A working manuscript with 7 pages and 8 figure

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 (Erd\H{o}s-R\'enyi, Barab\'asi-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.Comment: 23 pages, 12 figures, 3 table

Knitted Complex Networks

To a considerable extent, the continuing importance and popularity of complex networks as models of real-world structures has been motivated by scale free degree distributions as well as the respectively implied hubs. Being related to sequential connections of edges in networks, paths represent another important, dual pattern of connectivity (or motif) in complex networks (e.g., paths are related to important concepts such as betweeness centrality). The present work proposes a new supercategory of complex networks which are organized and/or constructed in terms of paths. Two specific network classes are proposed and characterized: (i) PA networks, obtained by star-path transforming Barabasi-Albert networks; and (ii) PN networks, built by performing progressive paths involving all nodes without repetition. Such new networks are important not only from their potential to provide theoretical insights, but also as putative models of real-world structures. The connectivity structure of these two models is investigated comparatively to four traditional complex networks models (Erdos-Renyi, Barabasi-Albert, Watts-Strogatz and a geographical model). A series of interesting results are described, including the corroboration of the distinct nature of the two proposed models and the importance of considering a comprehensive set of measurements and multivariated statistical methods for the characterization of complex networks.Comment: 10 pages, 5 figures, 1 table. A working manuscript, comments and suggestions welcome

On the Dynamics of the h−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.Comment: 9 pages, 1 table, 2 figures. A working manuscript: comments and criticisms are welcome

Activation Confinement Inside Complex Networks Communities

In this work it is described how to enhance and generalize the equivalent model (arXiv:0802.0421) of integrate-and-fire dynamics in order to treat any complex neuronal networks, especially those exibiting modular structure. It has been shown that, though involving only a handful of equivalent neurons, the modular equivalent model was capable of providing impressive predictions about the non-linear integrate-and-fire dynamics in two hybrid modular networks. The reported approach has also allowed the identification of the causes of transient spiking confinement within the network communities, which correspond to the fact that the little activation sent from the source community to the others implies in long times for reaching the nearly-simultaneous activation of the concentric levels at the other communities and respective avalanches. Several other insights are reported in this work, including the smoothing of the spiking functions, the consideration of intra-ring connections and its effects, as well as the identification of how the weights in the equivalent model change for different source nodes. This work has paved the way for a number of promising developments, which are identified and discussed. Preliminary results are also described which reveal waves induced by the integrate-and-fire dynamics along the steady-state regime.Comment: 18 pages, 15 figures. A working manuscrip. Suggestions and comments welcome

Hub-Based Community Finding

This article presents a hub-based approach to community finding in complex networks. After identifying the network nodes with highest degree (the so-called hubs), the network is flooded with wavefronts of labels emanating from the hubs, accounting for the identification of the involved communities. The simplicity and potential of this method, which is presented for direct/undirected and weighted/unweighted networks, is illustrated with respect to the Zachary karate club data, image segmentation, and concept association. Attention is also given to the identification of the boundaries between communities.Comment: 4 pages, 3 figures, 1 tabl