203 research outputs found
Embedding Graphs into Embedded Graphs
A (possibly degenerate) drawing of a graph G in the plane is approximable by an embedding if it can be turned into an embedding by an arbitrarily small perturbation.
We show that testing, whether a drawing of a planar graph G in the plane is approximable by an embedding, can be carried out in polynomial time, if a desired embedding of G belongs to a fixed isotopy class, i.e., the rotation system (or equivalently the faces) of the embedding of G and the choice of outer face are fixed.
In other words, we show that c-planarity with embedded pipes is tractable for graphs with fixed embeddings.
To the best of our knowledge an analogous result was previously known essentially only when G is a cycle
Constrained Planarity in Practice -- Engineering the Synchronized Planarity Algorithm
In the constrained planarity setting, we ask whether a graph admits a planar
drawing that additionally satisfies a given set of constraints. These
constraints are often derived from very natural problems; prominent examples
are Level Planarity, where vertices have to lie on given horizontal lines
indicating a hierarchy, and Clustered Planarity, where we additionally draw the
boundaries of clusters which recursively group the vertices in a crossing-free
manner. Despite receiving significant amount of attention and substantial
theoretical progress on these problems, only very few of the found solutions
have been put into practice and evaluated experimentally.
In this paper, we describe our implementation of the recent quadratic-time
algorithm by Bl\"asius et al. [TALG Vol 19, No 4] for solving the problem
Synchronized Planarity, which can be seen as a common generalization of several
constrained planarity problems, including the aforementioned ones. Our
experimental evaluation on an existing benchmark set shows that even our
baseline implementation outperforms all competitors by at least an order of
magnitude. We systematically investigate the degrees of freedom in the
implementation of the Synchronized Planarity algorithm for larger instances and
propose several modifications that further improve the performance. Altogether,
this allows us to solve instances with up to 100 vertices in milliseconds and
instances with up to 100 000 vertices within a few minutes.Comment: to appear in Proceedings of ALENEX 202
Real-time interactive visualization of large networks on a tiled display system
This paper introduces a methodology for visualizing large real-world (social) network data on a high-resolution tiled display system. Advances in network drawing algorithms enabled real-time visualization and interactive exploration of large real-world networks. However, visualization on a typical desktop monitor remains challenging due to the limited amount of screen space and ever increasing size of real-world datasets.To solve this problem, we propose an integrated approach that employs state-of-the-art network visual-ization algorithms on a tiled display system consisting of multiple screens. Key to our approach is to use the machine's graphics processing units (GPUs) to their fullest extent, in order to ensure an interactive setting with real-time visualization. To realize this, we extended a recent GPU-based implementation of a force-directed graph layout algorithm to multiple GPUs and combined this with a distributed rendering approach in which each graphics card in the tiled display system renders precisely the part of the network to be displayed on the monitors attached to it.Our evaluation of the approach on a 12-screen 25 megapixels tiled display system with three GPUs, demonstrates interactive performance at 60 frames per second for real-world networks with tens of thousands of nodes and edges. This constitutes a performance improvement of approximately 4 times over a single GPU implementation. All the software developed to implement our tiled visualization approach, including the multi-GPU network layout, rendering, display and interaction components, are made available as open-source software.Computer Systems, Imagery and Medi
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