We introduce multiforce, a force-directed layout for multiplex networks, where the nodes of the network are organized into multiple layers and both in-layer and inter-layer relationships among nodes are used to compute node coordinates. The proposed approach generalizes existing work, providing a range of intermediate layouts in-between the ones produced by known methods. Our experiments on real data show that multiforce can keep nodes well aligned across different layers without significantly affecting their internal layouts when the layers have similar or compatible topologies. As a consequence, multiforce enriches the benefits of force-directed layouts by also supporting the identification of topological correspondences between layers.
Many layouts have been designed to visualize monoplex networks. Here we briefly review the ones that are more relevant for our approach. For an extensive review, the reader may consult @cite_32 .
@cite_32
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Visual cognition of the indoor environment can benefit from the spatial layout estimation, which is to represent an indoor scene with a 2D box on a monocular image. In this paper, we propose to fully exploit the edge and semantic information of a room image for layout estimation. More specifically, we present an encoder-decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image. We combine these two network predictions in a scoring function to evaluate the quality of the layouts, which are generated by ray sampling and from a predefined layout pool. Guided by the scoring function, we apply a novel refinement strategy to further optimize the layout hypotheses. Experimental results show that the proposed network can yield accurate estimates of edge maps and semantic labels. By fully utilizing the two different types of labels, the proposed method achieves state-of-the-art layout estimation performance on benchmark datasets. Networks have remained a challenge for information visualization designers because of the complex issues of node and link layout coupled with the rich set of tasks that users present. This paper offers a strategy based on two principles: (1) layouts are based on user-defined semantic substrates, which are non-overlapping regions in which node placement is based on node attributes, (2) users interactively adjust sliders to control link visibility to limit clutter and thus ensure comprehensibility of source and destination. Scalability is further facilitated by user control of which nodes are visible. We illustrate our semantic substrates approach as implemented in NVSS 1.0 with legal precedent data for up to 1122 court cases in three regions with 7645 legal citations The topics discussed are minimization of the area occupied by a layout and related results concerning networks flow and rectilinear representation of planar graphs, based on a graph model of floorplans and layouts. Arbitrary floorplans are allowed. Given an arbitrary floorplan and the areas of the embedded building blocks, the existence and uniqueness of a zero wasted area layout are proved, and characterized by a necessary and sufficient condition. Based on this condition, a scheme is described to generate zero-wasted-area layouts. Given a family of dual network pairs for which the product of dual arc lengths are invariant, it is proved that the minimal product of their longest paths is not smaller than the maximal product of their shortest paths. It is also shown that the maximal product of the flows in such a family of dual network pairs is given by the total sum of the arc length product of each individual pair of dual arcs. An efficient procedure to derive a rectilinear representation for any planar graph is presented. > A standard approach for visualizing multivariate networks is to use one or more multidimensional views (for example, scatterplots) for selecting nodes by various metrics, possibly coordinated with a node-link view of the network. In this paper, we present three novel approaches for achieving a tighter integration of these views through hybrid techniques for multidimensional visualization, graph selection and layout. First, we present the FlowVizMenu, a radial menu containing a scatterplot that can be popped up transiently and manipulated with rapid, fluid gestures to select and modify the axes of its scatterplot. Second, the FlowVizMenu can be used to steer an attribute-driven layout of the network, causing certain nodes of a node-link diagram to move toward their corresponding positions in a scatterplot while others can be positioned manually or by force-directed layout. Third, we describe a novel hybrid approach that combines a scatterplot matrix (SPLOM) and parallel coordinates called the Parallel Scatterplot Matrix (P-SPLOM), which can be used to visualize and select features within the network. We also describe a novel arrangement of scatterplots called the Scatterplot Staircase (SPLOS) that requires less space than a traditional scatterplot matrix. Initial user feedback is reported.