Abstract
Distributed Computing is becoming more and more led by technological and application advances. Many works consider new computing models compared to the classical closed model with a fixed number of participants and strong hypothesis on communication and structuration. Indeed, it is hard to imagine some application or computational activity and process that falls outside Distributed Computing. Internet and the web (e.g. social networks, clouds) are becoming the main application field for distributed computing. In addition to the classical challenges that developers have to face (asynchrony and failures) they have to deal with load balancing, malicious and selfish behaviors, mobility, heterogeneity and the dynamic nature of participating processes. © 2013 Springer-Verlag.

Abstract

Abstract
The aggregation and estimation of values over networks is fundamental for distributed applications, such as wireless sensor networks. Estimating the average, minimal and maximal values has already been extensively studied in the literature. In this paper, we focus on estimating empirical distributions of values in a network with anonymous agents. In particular, we compare two different estimation strategies in terms of their convergence speed, accuracy and communication costs. The first strategy is deterministic and based on the average consensus protocol, while the second strategy is probabilistic and based on the max consensus protocol.

Abstract
In order to further improve the performance of current genetic algorithms aiming at discovering communities, a local search based genetic algorithm (GALS) is here proposed. The core of GALS is a local search based mutation technique. In order to overcome the drawbacks of traditional mutation methods, the paper develops the concept of marginal gene and then the local monotonicity of modularity function Q is deduced from each node's local view. Based on these two elements, a new mutation method combined with a local search strategy is presented. GALS has been evaluated on both synthetic benchmarks and several real networks, and compared with some presently competing algorithms. Experimental results show that GALS is highly effective and efficient for discovering community structure.

Abstract
Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities.

Abstract
This paper proposes a set of metrics for the assessment of the complexity of models defining the business logic of spreadsheets. This set can be considered the first step in the direction of building a quality standard for spreadsheet models, that is still to be defined. The computation of concrete metric values has further been integrated under a well-established model-driven spreadsheet development environment, providing a framework for the analysis of spreadsheet models under spreadsheets themselves.