Many automatic graph drawing algorithms implement only one or two aesthetic criteria since most aesthetics conflict with each other. Empirical research has shown that although those algorithms are based on different a...
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Many automatic graph drawing algorithms implement only one or two aesthetic criteria since most aesthetics conflict with each other. Empirical research has shown that although those algorithms are based on different aesthetics, drawings produced by them have comparable effectiveness. The comparable effectiveness raises a question about the necessity of choosing one algorithm against another for drawing graphs when human performance is a main concern. In this paper, we argue that effectiveness can be improved when algorithms are designed by making compromises between aesthetics, rather than trying to satisfy one or two of them to the fullest. We therefore introduce a new algorithm: BIGANGLE. This algorithm produces drawings with multiple aesthetics being improved at the same time, compared to a classical spring algorithm. A user study comparing these two algorithms indicates that BIGANGLE induces a significantly better task performance and a lower cognitive load, therefore resulting in better graph drawings in terms of human cognitive efficiency. Our study indicates that aesthetics should not be considered separately. Improving multiple aesthetics at the same time, even to small extents, will have a better chance to make resultant drawings more effective. Although this finding is based on a study of algorithms, it also applies in general graph visualization and evaluation. (C) 2011 Elsevier Ltd. All rights reserved.
Living systems can be envisioned as a beautiful fabric with weaves of different biological networks such as genetic, protein, transcription factors and metabolic interactions. The weave pattern or the network architec...
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Living systems can be envisioned as a beautiful fabric with weaves of different biological networks such as genetic, protein, transcription factors and metabolic interactions. The weave pattern or the network architecture is a precise predictor of healthy or diseased state of the organism. Network-based approach gives insight into pathogenesis pathway which leads to drug discovery process. It helps researchers and clinicians in grouping together the proteins that interact in functional complexes and pathways thus exploring disease network nodes as potential target for drug discovery. Biological data are often structured in the form of complex interconnected networks such as protein interaction and metabolic networks. Visualization tools help in the visualization of various interactome data. Sheer size, complexity and dynamic nature of networks, and the algorithm responsible for visualization are the main challenges of biological network visualization. Visualization tools are based on certain graph theories and algorithm for visualization. In the present work, directed and undirected graphs are used in the visualization of protein interactions data. To visualize time course behavior of genes, there is need to combine these two graph theories which can interpret biological phenomenon in better way. Further, force-directed algorithm is used for the visualization of protein interaction data. It helps in integrating interactome data and provides better visualization. To overcome this aspect, a holistic approach is required.
Students attending lectures in universities suffer from a weak structural awareness on lecture content. According to learning theories, structural awareness is a relevant factor to association and comprehension of new...
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ISBN:
(纸本)9789897582400
Students attending lectures in universities suffer from a weak structural awareness on lecture content. According to learning theories, structural awareness is a relevant factor to association and comprehension of new learning inputs. We synthesize semantic structures from non annotated lecture slides using Topic Modeling algorithms to identify relevant terms and relate them in force-directed graphs. The synthesized graphs provide a structural overview on the topic distribution and relations of non annotated sequential lecture slides.
The financial data is complex to analyse due to its complicated relationships and multiple attributes. Centrality measure models from the SNA (Social network analysis) can show the most critical variables in a network...
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ISBN:
(数字)9781510634107
ISBN:
(纸本)9781510634107
The financial data is complex to analyse due to its complicated relationships and multiple attributes. Centrality measure models from the SNA (Social network analysis) can show the most critical variables in a network, and graph layouts can be produced to represent not only data networks but also the relations among data entries. To the best of our knowledge, there is no work that has been tried on the Australian stock market based on the combination of those two methods mentioned above so far. This study adopts centrality measure methods and a graph drawing algorithm (force-directed) to offer users big pictures and detailed views, comes with ranking factors based on weighted degree, pagerank and eigenvector metrics. The outcomes show that the methodology can produce clear graph layouts of the stock's social network, identify the central stocks (represent through features such as node colour and size) and the business sectors they belong to. This study may assist stakeholders with grasping deep insight from the complex financial datasets, and another angle of view to adjust future investments accordingly.
We present a method by which force-directed algorithms for graph layouts can be generalized to calculate the layout of a graph in an arbitrary Riemannian geometry. The method relies on extending the Euclidean notions ...
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ISBN:
(纸本)0780387791
We present a method by which force-directed algorithms for graph layouts can be generalized to calculate the layout of a graph in an arbitrary Riemannian geometry. The method relies on extending the Euclidean notions of distance, angle, and force-interactions to smooth non-Euclidean geometries via projections to and from appropriately chosen tangent spaces. In particular, we formally describe the calculations needed to extend such algorithms to hyperbolic and spherical geometries.
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