1,721,153 research outputs found
The Turing Test for Graph Drawing Algorithms
No full textDo algorithms for drawing graphs pass the Turing Test? Thatis, are their outputs indistinguishable from graphs drawn by humans? Weaddress this question through a human-centred experiment, focusing on`small´ graphs, of a size for which it would be reasonable for someone tochoose to draw the graph manually. Overall, we find that hand-drawnlayouts can be distinguished from those generated by graph drawing al-gorithms, although this is not always the case for graphs drawn by force-directed or multi-dimensional scaling algorithms, making these good can-didates for Turing Test success. We show that, in general, hand-drawngraphs are judged to be of higher quality than automatically generatedones, although this result varies with graph size and algorithm
Generating Euler Diagrams Through Combinatorial Optimization
No full textCan a given set system be drawn as an Euler diagram? We present the first method that correctly decides this question for arbitrary set systems if the Euler diagram is required to represent each set with a single connected region. If the answer is yes, our method constructs an Euler diagram. If the answer is no, our method yields an Euler diagram for a simplified version of the set system, where a minimum number of set elements have been removed. Further, we integrate known wellformedness criteria for Euler diagrams as additional optimization objectives into our method. Our focus lies on the computation of a planar graph that is embedded in the plane to serve as the dual graph of the Euler diagram. Since even a basic version of this problem is known to be NP-hard, we choose an approach based on integer linear programming (ILP), which allows us to compute optimal solutions with existing mathematical solvers. For this, we draw upon previous research on computing planar supports of hypergraphs and adapt existing ILP building blocks for contiguity-constrained spatial unit allocation and the maximum planar subgraph problem. To generate Euler diagrams for large set systems, for which the proposed simplification through element removal becomes indispensable, we also present an efficient heuristic. We report on experiments with data from MovieDB and Twitter. Over all examples, including 850 non-trivial instances, our exact optimization method failed only for one set system to find a solution without removing a set element. However, with the removal of only a few set elements, the Euler diagrams can be substantially improved with respect to our wellformedness criteria.Computer Graphics ForumGeospatial Data and Optimization43
Evaluation in the crowd: an introduction
No full textHuman-centred empirical evaluations play important roles in the fields of human-computer interaction, visualisation, and graphics. The advent of crowdsourcing platforms, such as Amazon Mechanical Turk, has provided a revolutionary methodology to conduct human-centred experiments. Through such platforms, experiments can now collect data from hundreds, even thousands, of participants from a diverse user community over a matter of weeks, greatly increasing the ease with which we can collect data as well as the power and generalisability of experimental results. However, such an experimental platform does not come without its problems: ensuring participant investment in the task, defining experimental controls, and understanding the ethics behind deploying such experiments en masse. This book is intended to be a primer for computer science researchers who intend to use crowdsourcing technology for human centred experiments. It focuses on methodological considerations when using crowdsourcing platforms to run human-centred experiments, particularly in the areas of visualisation and of quality of experience (QoE) for online video delivery. We hope that this book can act as a primer to researchers in our fields that intend to run experiments on crowdsourcing for the purposes of human-centred experimentation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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