Drawing Graphs within Restricted Area

We study the problem of selecting a maximum-weight subgraph of a given graph such that the subgraph can be drawn within a prescribed drawing area subject to given non-uniform vertex sizes. We develop and analyze heuristics both for the general (undirected) case and for the use case of (directed) calculation graphs which are used to analyze the typical mistakes that high school students make when transforming mathematical expressions in the process of calculating, for example, sums of fractions

Gabriel Triangulations and Angle-Monotone Graphs: Local Routing and Recognition

A geometric graph is angle-monotone if every pair of vertices has a path between them that---after some rotation---is $x$- and $y$-monotone. Angle-monotone graphs are $\sqrt 2$-spanners and they are increasing-chord graphs. Dehkordi, Frati, and Gudmundsson introduced angle-monotone graphs in 2014 and proved that Gabriel triangulations are angle-monotone graphs. We give a polynomial time algorithm to recognize angle-monotone geometric graphs. We prove that every point set has a plane geometric graph that is generalized angle-monotone---specifically, we prove that the half-$\theta_6$-graph is generalized angle-monotone. We give a local routing algorithm for Gabriel triangulations that finds a path from any vertex $s$ to any vertex $t$ whose length is within $1 + \sqrt 2$ times the Euclidean distance from $s$ to $t$. Finally, we prove some lower bounds and limits on local routing algorithms on Gabriel triangulations.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

Drawing Arrangement Graphs In Small Grids, Or How To Play Planarity

We describe a linear-time algorithm that finds a planar drawing of every graph of a simple line or pseudoline arrangement within a grid of area O(n^{7/6}). No known input causes our algorithm to use area \Omega(n^{1+\epsilon}) for any \epsilon>0; finding such an input would represent significant progress on the famous k-set problem from discrete geometry. Drawing line arrangement graphs is the main task in the Planarity puzzle.Comment: 12 pages, 8 figures. To appear at 21st Int. Symp. Graph Drawing, Bordeaux, 201

The Widths of Strict Outerconfluent Graphs

Strict outerconfluent drawing is a style of graph drawing in which vertices are drawn on the boundary of a disk, adjacencies are indicated by the existence of smooth curves through a system of tracks within the disk, and no two adjacent vertices are connected by more than one of these smooth tracks. We investigate graph width parameters on the graphs that have drawings in this style. We prove that the clique-width of these graphs is unbounded, but their twin-width is bounded.Comment: 15 pages, 2 figure

Visualization of Large Networks Using Recursive Community Detection

Networks show relationships between people or things. For instance, a person has a social network of friends, and websites are connected through a network of hyperlinks. Networks are most commonly represented as graphs, so graph drawing becomes significant for network visualization. An effective graph drawing can quickly reveal connections and patterns within a network that would be difficult to discern without visual aid. But graph drawing becomes a challenge for large networks. Am- biguous edge crossings are inevitable in large networks with numerous nodes and edges, and large graphs often become a complicated tangle of lines. These issues greatly reduce graph readability and makes analyzing complex networks an arduous task. This project aims to address the large network visualization problem by com- bining recursive community detection, node size scaling, layout formation, labeling, edge coloring, and interactivity to make large graphs more readable. Experiments are performed on five known datasets to test the effectiveness of the proposed approach. A survey of the visualization results is conducted to measure the results

Stabbing line segments with disks: complexity and approximation algorithms

Computational complexity and approximation algorithms are reported for a problem of stabbing a set of straight line segments with the least cardinality set of disks of fixed radii $r>0$ where the set of segments forms a straight line drawing $G=(V,E)$ of a planar graph without edge crossings. Close geometric problems arise in network security applications. We give strong NP-hardness of the problem for edge sets of Delaunay triangulations, Gabriel graphs and other subgraphs (which are often used in network design) for $r\in [d_{\min},\eta d_{\max}]$ and some constant $\eta$ where $d_{\max}$ and $d_{\min}$ are Euclidean lengths of the longest and shortest graph edges respectively. Fast $O(|E|\log|E|)$-time $O(1)$-approximation algorithm is proposed within the class of straight line drawings of planar graphs for which the inequality $r\geq \eta d_{\max}$ holds uniformly for some constant $\eta>0,$ i.e. when lengths of edges of $G$ are uniformly bounded from above by some linear function of $r.$Comment: 12 pages, 1 appendix, 15 bibliography items, 6th International Conference on Analysis of Images, Social Networks and Texts (AIST-2017

An energy-based model to optimize cluster visualization

National audienceGraphs are mathematical structures that provide natural means for complex-data representation. Graphs capture the structure and thus help modeling a wide range of complex real-life data in various domains. Moreover graphs are especially suitable for information visualization. Indeed the intuitive visualabstraction (dots and lines) they provide is intimately associated with graphs. Visualization paves the way to interactive exploratory data-analysis and to important goals such as identifying groups and subgroups among data and helping to understand how these groups interact with each other. In this paper, we present a graph drawing approach that helps to better appreciate the cluster structure in data and the interactions that may exist between clusters. In this work, we assume that the clusters are already extracted and focus rather on the visualization aspects. We propose an energy-based model for graph drawing that produces an esthetic drawing that ensures each cluster will occupy a separate zone within thevisualization layout. This method emphasizes the inter-groups interactions and still shows the inter-nodes interactions. The drawing areas assigned to the clusters can be user-specified (prefixed areas) or automatically crafted (free areas). The approach we suggest also enables handling geographically-based clustering. In the case of free areas, we illustrate the use of our drawing method through an example. In the case of prefixed areas, we firstuse an example from citation networks and then use another exampleto compare the results of our method to those of the divide and conquer approach. In the latter case, we show that while the two methods successfully point out the cluster structure our method better visualize the global structure

OCDaf: Ordered Causal Discovery with Autoregressive Flows

We propose OCDaf, a novel order-based method for learning causal graphs from observational data. We establish the identifiability of causal graphs within multivariate heteroscedastic noise models, a generalization of additive noise models that allow for non-constant noise variances. Drawing upon the structural similarities between these models and affine autoregressive normalizing flows, we introduce a continuous search algorithm to find causal structures. Our experiments demonstrate state-of-the-art performance across the Sachs and SynTReN benchmarks in Structural Hamming Distance (SHD) and Structural Intervention Distance (SID). Furthermore, we validate our identifiability theory across various parametric and nonparametric synthetic datasets and showcase superior performance compared to existing baselines

Using Behavior Over Time Graphs to Spur Systems Thinking Among Public Health Practitioners

Public health practitioners can use Behavior Over Time (BOT) graphs to spur discussion and systems thinking around complex challenges. Multiple large systems, such as health care, the economy, and education, affect chronic disease rates in the United States. System thinking tools can build public health practitioners’ capacity to understand these systems and collaborate within and across sectors to improve population health. BOT graphs show a variable, or variables (y axis) over time (x axis). Although analyzing trends is not new to public health, drawing BOT graphs, annotating the events and systemic forces that are likely to influence the depicted trends, and then discussing the graphs in a diverse group provides an opportunity for public health practitioners to hear each other’s perspectives and creates a more holistic understanding of the key factors that contribute to a trend. We describe how BOT graphs are used in public health, how they can be used to generate group discussion, and how this process can advance systems-level thinking. Then we describe how BOT graphs were used with groups of maternal and child health (MCH) practitioners and partners (N = 101) during a training session to advance their thinking about MCH challenges. Eighty-six percent of the 84 participants who completed an evaluation agreed or strongly agreed that they would use this BOT graph process to engage stakeholders in their home states and jurisdictions. The BOT graph process we describe can be applied to a variety of public health issues and used by practitioners, stakeholders, and researchers