Summary Social network analysis is an interdisciplinary topic attracting researchers from biology, economics, psychology, and machine learning, with an existing long history based on graph theory. It has since attracted interests from both the research and business communities for a strong potential and variety of applications. In addition, this interest has been fueled by the large success of online social networking sites and the subsequent abundance of social network data produced. An important aspect in this research field is influence maximization in social networks. The goal is to find a set of individuals to be targeted with the aim to drive social contagion and generate a diffusion cascade. We provide here an overview of the models and approaches used to analyze social networks. In this context, we also discuss data preparation and privacy concerns. We further describe different kind of approaches based on centrality measures, which express a sociological interpretation of the data, and stochastic influence and information propagation techniques, which aim at modeling the underlying diffusion processes that govern social interactions.

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In many key applications of metabolomics, such as toxicology or nutrigenomics, it is of interest to profile and detect changes in metabolic processes, usually represented in the form of pathways. As an alternative, a broader point of view would enable investigators to better understand the relations between entities that exist in different processes. Therefore, relating a possible perturbation to several known processes represents a new approach to this field of study. We propose to use a network representation of metabolism in terms of reactants, enzyme and metabolite. To model these systems it is possible to describe both reactions and relations among enzymes and metabolites. In this way, analysis of the impact of changes in some metabolites or enzymes on different processes are easier to understand, detect and predict. Results: We release the MetaboX library, an open source PHP framework for developing metabolic networks from a set of compounds. This library uses data stored in Kyoto Encyclopedia for Genes and Genomes (KEGG) database using its RESTful Application Programming Interfaces (APIs), and methods to enhance manipulation of the information returned from KEGG webservice. The MetaboX library includes methods to extract information about a resource of interest (e.g. metabolite, reaction and enzyme) and to build reactants networks, bipartite enzyme-metabolite and unipartite enzyme networks. These networks can be exported in different formats for data visualization with standard tools. As a case study, the networks built from a subset of the Glycolysis pathway are described and discussed. Conclusions: The advantages of using such a library imply the ability to model complex systems with few starting information represented by a collection of metabolites KEGG IDs.

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Sophisticated denoising algorithms are used to improve image quality in the Magnetic Resonance Imaging field. Of course, better results are obtained by implementing computationally expensive schemes. In this paper, we consider the Overcomplete Local Principal Component Analysis (OLPCA) method for image denoising and its main issues. More in detail, we investigated the impact of the Singular Value Decomposition on the OLPCA algorithm and its high computational cost. Moreover, we propose a fine-to-coarse parallelization strategy in order to exploit a parallel hybrid architecture and we implement a multilevel parallel software as a smart combination between codes using NVIDIA cuBLAS library for Graphic Processor Units (GPUs) and the standard Message Passing Interface library for cluster programming. Experimental results show improvements in terms of execution time with a promising speed up with respect to the CPU and our old GPU versions