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Visualization strategies facilitate streaming network analysis by allowing its exploration through graphical and interactive layouts. Depending on the strategy and the network density, such layouts may suffer from a high level of visual clutter that hides meaningful temporal patterns, highly active groups of nodes, bursts of activity, and other important network properties. Edge sampling improves layout readability, highlighting important properties and leading to easier and faster pattern identification and decision making. This paper presents Streaming Edge Sampling for Network Visualization–SEVis, a streaming edge sampling method that discards edges of low-active nodes while preserving a distribution of edge counts that is similar to the original network. It can be applied to a variety of layouts to enhance streaming network analyses. We evaluated SEVis performance using synthetic and real-world networks through quantitative and visual analyses. The results indicate a higher performance of SEVis for clutter reduction and pattern identification when compared with other sampling methods.

Networks are often used to model the structure of interactions between parts of a system. One important characteristic of a network is the so-called network community structures that are groups of nodes more connected between themselves than with nodes from other groups. Such community structure is fundamental to better understand the organization of networks. Although there are several community detection algorithms in the literature, choosing the most appropriate for a specific task is not always trivial. This paper introduces a methodology to analyze the performance of community detection algorithms using network visualization. We assess the methodology using two widely adopted community detection algorithms: Infomap and Louvain. We apply both algorithms to four real-world networks with a variety of characteristics to demonstrate the usefulness and generality of the methodology. We discuss the performance of these algorithms and show how the user may use statistical and visual analytics to identify the most appropriate network community detection algorithm for a certain network analysis task.

Nitrous oxide (N2O) is an important greenhouse gas. Emissions of this gas are generated by the combustion of fossil fuels, mainly in the transportation sector worldwide. Biofuel is a promising alternative instead of diesel considering that it allows a reduction on the emissions of polluting gases. In Brazil, there is a large program on the use of biofuels, which according to the Intergovernmental Panel on Climate Change (IPCC) reduces greenhouse gas emissions. This work shows the N2O emissions resulting from combustions of diesel/beef tallow biodiesel blends of B7, B15, B20, B25, B35, and B50. In parallel, another experiment was done with fuel sample adopted by a bus fleet from Sao Paulo using blends comprising 10% of sugarcane diesel in diesel (CD10) and blends with beef tallow biodiesel of CD10: CD-B15, CD-B20, CD-B25, CD-B35, and CD-B50. The photoacoustic spectroscopy technique coupled to a quantum cascade laser (QCL) was used to detect N2O. Concentrations of the emissions were detected in the range of 5.3 to 7.4 ppmv for diesel/beef tallow biodiesel blends combustion and in the range of 4.3 to 11.0 ppmv for diesel/sugar cane diesel/beef tallow biodiesel blends combustion. The technique used was very sensitive and selective to identify and determine the emission concentrations of N2O.