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Evaluations—encompassing computational evaluations, benchmarks and user studies—are essential tools for validating the performance and applicability of graph and network layout algorithms (also known as graph drawing). These evaluations not only offer significant insights into an algorithm's performance and capabilities, but also assist the reader in determining if the algorithm is suitable for a specific purpose, such as handling graphs with a high volume of nodes or dense graphs. Unfortunately, there is no standard approach for evaluating layout algorithms. Prior work holds a ‘Wild West’ of diverse benchmark datasets and data characteristics, as well as varied evaluation metrics and ways to report results. It is often difficult to compare layout algorithms without first implementing them and then running your own evaluation. In this systematic review, we delve into the myriad of methodologies employed to conduct evaluations—the utilized techniques, reported outcomes and the pros and cons of choosing one approach over another. Our examination extends beyond computational evaluations, encompassing user‐centric evaluations, thus presenting a comprehensive understanding of algorithm validation. This systematic review—and its accompanying website—guides readers through evaluation types, the types of results reported, and the available benchmark datasets and their data characteristics. Our objective is to provide a valuable resource for readers to understand and effectively apply various evaluation methods for graph layout algorithms. A free copy of this paper and all supplemental material is available at osf.io, and the categorized papers are accessible on our website at https://visdunneright.github.io/gd-comp-eval/ 

Survey companion websites allow users to explore collected survey information more deeply, as well as update or add entries for papers. These sites can help information stay relevant past the original release date of the survey paper. However, creating and maintaining a website can be laborious and difficult, especially when authors might not be experienced with programming. We introduce Indy Survey Tool to help authors develop companion websites for survey papers across diverse fields of study. The tool's core aim is to identify correlations between categorizations of papers. To accomplish this, the tool offers multiple combined filters and correlation matrix visualizations that enable users to explore the data from diverse perspectives. The tool's visualizations, list of papers, and filters are harmoniously integrated and highly responsive, providing users with feedback based on their selections. Identifying correlations in survey papers is a pivotal aspect of research, as it can enable the recognition of common combinations of categorizations within the papers—as well as highlight any omissions. The versatility of Indy Survey Tool enables researchers to delve into the correlations between categorizations in survey data, an essential aspect of research that can reveal gaps in the literature and highlight promising areas for future exploration. A preprint and supplemental material for the paper can be found at osf.io/tdhqn. 

We built a website to help graph drawing researchers find benchmark datasets to use for evaluating graph layout algorithms. 

Abstract Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counterintuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfvén waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold,α= 2 as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed >600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: preflare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine thatα= 1.63 ± 0.03. This is below the critical threshold, suggesting that Alfvén waves are an important driver of coronal heating.