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.Comment: 5 pages, 2 figure

Similarity Means: A Study on Stability and Symmetry

The arithmetic mean plays a central role in science and technology, being directly related to the concepts of statistical expectance and centrality. Yet, it is highly susceptible to the presence of ouliers or biased interference in the original dataset to which it is applied. Described recently, the concept of similarity means has been preliminary found to have marked robustness to those same effects, especially when adopting the Jaccard similarity index. The present work is aimed at investigating further the properties of similarity means, especially regarding their range, translating and scaling properties, sensitivity and robustness to outliers. Several interesting contributions are reported, including an effective algorithm for obtaining the similarity mean, the analytic and experimental identification of a number of properties, as well as the confirmation of the potential stability of the similarity mean to the presence of outliers. The present work also describes an application case-example in which the Jaccard similarity is succesfully employed to study cycles of sunspots, with interesting results

Evaluating links through spectral decomposition

Spectral decomposition has been rarely used to investigate complex networks. In this work we apply this concept in order to define two types of link-directed attacks while quantifying their respective effects on the topology. Several other types of more traditional attacks are also adopted and compared. These attacks had substantially diverse effects, depending on each specific network (models and real-world structures). It is also showed that the spectral-based attacks have special effect in affecting the transitivity of the networks

On the Efficiency of Data Representation on the Modeling and Characterization of Complex Networks

Specific choices about how to represent complex networks can have a substantial effect on the execution time required for the respective construction and analysis of those structures. In this work we report a comparison of the effects of representing complex networks statically as matrices or dynamically as spase structures. Three theoretical models of complex networks are considered: two types of Erdos-Renyi as well as the Barabasi-Albert model. We investigated the effect of the different representations with respect to the construction and measurement of several topological properties (i.e. degree, clustering coefficient, shortest path length, and betweenness centrality). We found that different forms of representation generally have a substantial effect on the execution time, with the sparse representation frequently resulting in remarkably superior performance