Graph Drawing Tutorial
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Networkx Tutorial
5.03.2020 tutorial NetworkX tutorial Source: https://github.com/networkx/notebooks (https://github.com/networkx/notebooks) Minor corrections: JS, 27.02.2019 Creating a graph Create an empty graph with no nodes and no edges. In [1]: import networkx as nx In [2]: G = nx.Graph() By definition, a Graph is a collection of nodes (vertices) along with identified pairs of nodes (called edges, links, etc). In NetworkX, nodes can be any hashable object e.g. a text string, an image, an XML object, another Graph, a customized node object, etc. (Note: Python's None object should not be used as a node as it determines whether optional function arguments have been assigned in many functions.) Nodes The graph G can be grown in several ways. NetworkX includes many graph generator functions and facilities to read and write graphs in many formats. To get started though we'll look at simple manipulations. You can add one node at a time, In [3]: G.add_node(1) add a list of nodes, In [4]: G.add_nodes_from([2, 3]) or add any nbunch of nodes. An nbunch is any iterable container of nodes that is not itself a node in the graph. (e.g. a list, set, graph, file, etc..) In [5]: H = nx.path_graph(10) file:///home/szwabin/Dropbox/Praca/Zajecia/Diffusion/Lectures/1_intro/networkx_tutorial/tutorial.html 1/18 5.03.2020 tutorial In [6]: G.add_nodes_from(H) Note that G now contains the nodes of H as nodes of G. In contrast, you could use the graph H as a node in G. -
Networkx: Network Analysis with Python
NetworkX: Network Analysis with Python Salvatore Scellato Full tutorial presented at the XXX SunBelt Conference “NetworkX introduction: Hacking social networks using the Python programming language” by Aric Hagberg & Drew Conway Outline 1. Introduction to NetworkX 2. Getting started with Python and NetworkX 3. Basic network analysis 4. Writing your own code 5. You are ready for your project! 1. Introduction to NetworkX. Introduction to NetworkX - network analysis Vast amounts of network data are being generated and collected • Sociology: web pages, mobile phones, social networks • Technology: Internet routers, vehicular flows, power grids How can we analyze this networks? Introduction to NetworkX - Python awesomeness Introduction to NetworkX “Python package for the creation, manipulation and study of the structure, dynamics and functions of complex networks.” • Data structures for representing many types of networks, or graphs • Nodes can be any (hashable) Python object, edges can contain arbitrary data • Flexibility ideal for representing networks found in many different fields • Easy to install on multiple platforms • Online up-to-date documentation • First public release in April 2005 Introduction to NetworkX - design requirements • Tool to study the structure and dynamics of social, biological, and infrastructure networks • Ease-of-use and rapid development in a collaborative, multidisciplinary environment • Easy to learn, easy to teach • Open-source tool base that can easily grow in a multidisciplinary environment with non-expert users -
Algorithms for Graph Visualization Flow Methods: Orthogonal Layout
Algorithms for Graph Visualization Flow Methods: Orthogonal Layout INSTITUT FUR¨ THEORETISCHE INFORMATIK · FAKULTAT¨ FUR¨ INFORMATIK Tamara Mchedlidze 18.01.2018 1 Dr. Tamara Mchedlidze · Algorithmen zur Visualisierung von Graphen Flussmethoden: orthogonales Graphenzeichnen Orthogonal Layout • Edges consist of vertical and horizontal segments • Applied in many areas ER diagram in OGDF UML diagram by Oracle Organigram of HS Limburg Circuit diagram by Jeff Atwood 2 - 1 Dr. Tamara Mchedlidze · Algorithmen zur Visualisierung von Graphen Flussmethoden: orthogonales Graphenzeichnen Orthogonal Layout • Edges consist of vertical and horizontal segments • Applied in many areas Aesthetic functions? ER diagram in OGDF UML diagram by Oracle Organigram of HS Limburg Circuit diagram by Jeff Atwood 2 - 2 Dr. Tamara Mchedlidze · Algorithmen zur Visualisierung von Graphen Flussmethoden: orthogonales Graphenzeichnen Orthogonal Layout • Edges consist of vertical and horizontal segments • Applied in many areas Aesthetic functions: • number of bends • length of edges • ER diagram in OGDF width, height, area • monotonicity of edges UML diagram by Oracle • ... Organigram of HS Limburg Circuit diagram by Jeff Atwood 2 - 3 Dr. Tamara Mchedlidze · Algorithmen zur Visualisierung von Graphen Flussmethoden: orthogonales Graphenzeichnen (Planar) Orthogonal Drawings Three-step approach: Topology { Shape { Metrics [Tamassia SIAM J. Comput. 1987] V = fv1; v2; v3; v4g E = fv1v2; v1v3; v1v4; v2v3; v2v4g 3 - 1 Dr. Tamara Mchedlidze · Algorithmen zur Visualisierung von Graphen Flussmethoden: orthogonales Graphenzeichnen (Planar) Orthogonal Drawings Three-step approach: Topology { Shape { Metrics [Tamassia SIAM J. Comput. 1987] V = fv1; v2; v3; v4g E = fv1v2; v1v3; v1v4; v2v3; v2v4g combinatorial embedding/ planarization 4 3 Reduce Crossings 2 1 3 - 2 Dr. Tamara Mchedlidze · Algorithmen zur Visualisierung von Graphen Flussmethoden: orthogonales Graphenzeichnen (Planar) Orthogonal Drawings Three-step approach: Topology { Shape { Metrics [Tamassia SIAM J. -
Graphprism: Compact Visualization of Network Structure
GraphPrism: Compact Visualization of Network Structure Sanjay Kairam, Diana MacLean, Manolis Savva, Jeffrey Heer Stanford University Computer Science Department {skairam, malcdi, msavva, jheer}@cs.stanford.edu GraphPrism Connectivity ABSTRACT nodeRadius 7.9 strokeWidth 1.12 charge -242 Visual methods for supporting the characterization, com- gravity 0.65 linkDistance 20 parison, and classification of large networks remain an open Transitivity updateGraph Close Controls challenge. Ideally, such techniques should surface useful 0 node(s) selected. structural features { such as effective diameter, small-world properties, and structural holes { not always apparent from Density either summary statistics or typical network visualizations. In this paper, we present GraphPrism, a technique for visu- Conductance ally summarizing arbitrarily large graphs through combina- tions of `facets', each corresponding to a single node- or edge- specific metric (e.g., transitivity). We describe a generalized Jaccard approach for constructing facets by calculating distributions of graph metrics over increasingly large local neighborhoods and representing these as a stacked multi-scale histogram. MeetMin Evaluation with paper prototypes shows that, with minimal training, static GraphPrism diagrams can aid network anal- ysis experts in performing basic analysis tasks with network Created by Sanjay Kairam. Visualization using D3. data. Finally, we contribute the design of an interactive sys- Figure 1: GraphPrism and node-link diagrams for tem using linked selection between GraphPrism overviews the largest component of a co-authorship graph. and node-link detail views. Using a case study of data from a co-authorship network, we illustrate how GraphPrism fa- compactly summarizing networks of arbitrary size. Graph- cilitates interactive exploration of network data. -
Graph Drawing Dagstuhl Seminar
05191 Abstracts Collection Graph Drawing Dagstuhl Seminar Michael Jünger1, Stephen Kobourov2 and Petra Mutzel3 1 Univ. of Köln, DE 2 Univ. of Arizona, US [email protected] 3 Univ. of Dortmund, DE [email protected] Abstract. From 08.05. to 13.05.05, the Dagstuhl Seminar 05191 Graph Drawing was held in the International Conference and Research Cen- ter (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The rst section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available. Keywords. Graph Drawing, Visualization, Layout Algorithms, Inter- active Visualization 05191 Executive Summary Graph Drawing This paper summarizes the topics, aims, and achievements of the Dagstuhl Sem- inar 05191 on Graph Drawing. Keywords: Graph drawing Joint work of: Jünger, Michael; Kobourov, Stephen; Mutzel, Petra Full Paper: http://drops.dagstuhl.de/opus/volltexte/2006/342 05191 Open Problem Session Report This is a report on an informal session intended to stimulate communication and sharing of problems. Hence the attributions and citations may contain inaccu- racies, and are certainly not complete. Keywords: Open problems, graph drawing Joint work of: Whitesides, Sue Full Paper: http://drops.dagstuhl.de/opus/volltexte/2006/338 Dagstuhl Seminar Proceedings 05191 Graph Drawing http://drops.dagstuhl.de/opus/volltexte/2006/348 2 M. Jünger, S. Kobourov and P. Mutzel Matrix Zoom: A Visual Interface to Semi-external Graphs James Abello (Rutgers Univ. -
Graph Simplification and Interaction
Graph Clarity, Simplification, & Interaction http://i.imgur.com/cW19IBR.jpg https://www.reddit.com/r/CableManagement/ Today • Today’s Reading: Lombardi Graphs – Bezier Curves • Today’s Reading: Clustering/Hierarchical edge Bundling – Definition of Betweenness Centrality • Emergency Management Graph Visualization – Sean Kim’s masters project • Reading for Tuesday & Homework 3 • Graph Interaction Brainstorming Exercise "Lombardi drawings of graphs", Duncan, Eppstein, Goodrich, Kobourov, Nollenberg, Graph Drawing 2010 • Circular arcs • Perfect angular resolution (edges for equal angles at vertices) • Arcs only intersect 2 vertices (at endpoints) • (not required to be crossing free) • Vertices may be constrained to lie on circle or concentric circles • People are more patient with aesthetically pleasing graphs (will spend longer studying to learn/draw conclusions) • What about relaxing the circular arc requirement and allowing Bezier arcs? • How does it scale to larger data? • Long curved arcs can be much harder to follow • Circular layout of nodes is often very good! • Would like more pseudocode Cubic Bézier Curve • 4 control points • Curve passes through first & last control point • Curve is tangent at P0 to (P1-P0) and at P3 to (P3-P2) http://www.e-cartouche.ch/content_reg/carto http://www.webreference.com/dla uche/graphics/en/html/Curves_learningObject b/9902/bezier.html 2.html “Force-directed Lombardi-style graph drawing”, Chernobelskiy et al., Graph Drawing 2011. • Relaxation of the Lombardi Graph requirements • “straight-line segments -
Constraint Graph Drawing
Constrained Graph Drawing Dissertation zur Erlangung des akademischen Grades des Doktors der Naturwissenschaften vorgelegt von Barbara Pampel, geb. Schlieper an der Universit¨at Konstanz Mathematisch-Naturwissenschaftliche Sektion Fachbereich Informatik und Informationswissenschaft Tag der mundlichen¨ Prufung:¨ 14. Juli 2011 1. Referent: Prof. Dr. Ulrik Brandes 2. Referent: Prof. Dr. Michael Kaufmann II Teile dieser Arbeit basieren auf Ver¨offentlichungen, die aus der Zusammenar- beit mit anderen Wissenschaftlerinnen und Wissenschaftlern entstanden sind. Zu allen diesen Inhalten wurden wesentliche Beitr¨age geleistet. Kapitel 3 (Bachmaier, Brandes, and Schlieper, 2005; Brandes and Schlieper, 2009) Kapitel 4 (Brandes and Pampel, 2009) Kapitel 6 (Brandes, Cornelsen, Pampel, and Sallaberry, 2010b) Zusammenfassung Netzwerke werden in den unterschiedlichsten Forschungsgebieten zur Repr¨asenta- tion relationaler Daten genutzt. Durch geeignete mathematische Methoden kann man diese Netzwerke als Graphen darstellen. Ein Graph ist ein Gebilde aus Kno- ten und Kanten, welche die Knoten verbinden. Hierbei k¨onnen sowohl die Kan- ten als auch die Knoten weitere Informationen beinhalten. Diese Informationen k¨onnen den einzelnen Elementen zugeordnet sein, sich aber auch aus Anordnung und Verteilung der Elemente ergeben. Mit Algorithmen (strukturierten Reihen von Arbeitsanweisungen) aus dem Gebiet des Graphenzeichnens kann man die unterschiedlichsten Informationen aus verschiedenen Forschungsbereichen visualisieren. Graphische Darstellungen k¨onnen das -
The Open Graph Drawing Framework (OGDF)
17 The Open Graph Drawing Framework (OGDF) Markus Chimani Friedrich-Schiller-Universit¨at Jena 17.1 Introduction::::::::::::::::::::::::::::::::::::::::::::::::: 543 • Carsten Gutwenger The History of the OGDF Outline TU Dortmund 17.2 Major Design Concepts:::::::::::::::::::::::::::::::::::: 544 Modularization • Self-Contained and Portable Source Code Michael J¨unger 17.3 General Algorithms and Data Structures ::::::::::::::: 546 • University of Cologne Augmentation and Subgraph Algorithms Graph Decomposition • Planarity and Planarization Gunnar W. Klau 17.4 Graph Drawing Algorithms ::::::::::::::::::::::::::::::: 550 • Centrum Wiskunde & Planar Drawing Algorithms Hierarchical Drawing • • Informatica Algorithms Energy-Based Drawing Algorithms Drawing Clustered Graphs Karsten Klein 17.5 Success Stories :::::::::::::::::::::::::::::::::::::::::::::: 562 • • TU Dortmund SPQR-Trees Exact Crossing Minimization Upward Graph Drawing Petra Mutzel Acknowledgments :::::::::::::::::::::::::::::::::::::::::::::::::: 564 TU Dortmund References :::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 565 17.1 Introduction We present the Open Graph Drawing Framework (OGDF), a C++ library of algorithms and data structures for graph drawing. The ultimate goal of the OGDF is to help bridge the gap between theory and practice in the field of automatic graph drawing. The library offers a wide variety of algorithms and data structures, some of them requiring complex and involved implementations, e.g., algorithms for planarity testing and planarization, or data structures for graph decomposition. A substantial part of these algorithms and data structures are building blocks of graph drawing algorithms, and the OGDF aims at pro- viding such functionality in a reusable form, thus also providing a powerful platform for implementing new algorithms. The OGDF can be obtained from its website at: http://www.ogdf.net This website also provides further information like tutorials, examples, contact information, and links to related projects. -
The Graph Crossing Number and Its Variants: a Survey
The Graph Crossing Number and its Variants: A Survey Marcus Schaefer School of Computing DePaul University Chicago, Illinois 60604, USA [email protected] Submitted: Dec 20, 2011; Accepted: Apr 4, 2013; Published: April 17, 2013 Third edition, Dec 22, 2017 Mathematics Subject Classifications: 05C62, 68R10 Abstract The crossing number is a popular tool in graph drawing and visualization, but there is not really just one crossing number; there is a large family of crossing number notions of which the crossing number is the best known. We survey the rich variety of crossing number variants that have been introduced in the literature for purposes that range from studying the theoretical underpinnings of the crossing number to crossing minimization for visualization problems. 1 So, Which Crossing Number is it? The crossing number, cr(G), of a graph G is the smallest number of crossings required in any drawing of G. Or is it? According to a popular introductory textbook on combi- natorics [460, page 40] the crossing number of a graph is “the minimum number of pairs of crossing edges in a depiction of G”. So, which one is it? Is there even a difference? To start with the second question, the easy answer is: yes, obviously there is a differ- ence, the difference between counting all crossings and counting pairs of edges that cross. But maybe these different ways of counting don’t make a difference and always come out the same? That is a harder question to answer. Pach and Tóth in their paper “Which Crossing Number is it Anyway?” [369] coined the term pair crossing number, pcr, for the crossing number in the second definition. -
A New Approximation Algorithm for Bend Minimization in the Kandinsky Model
A New Approximation Algorithm for Bend Minimization in the Kandinsky Model Wilhelm Barth1, Petra Mutzel2, and Canan Yıldız1 1 Institute of Computer Graphics and Algorithms, Vienna University of Technology Favoritenstraße 9-11, 1040 Wien, Austria {barth,yildizca}@ads.tuwien.ac.at http://www.ads.tuwien.ac.at/ 2 Department of Computer Science, University of Dortmund Otto-Hahn-Str. 14, D-44227 Dortmund, Germany [email protected] http://ls11-www.cs.uni-dortmund.de/ Abstract. The Kandinsky model has been introduced by F¨oßmeier and Kaufmann in order to deal with planar orthogonal drawings of planar graphs with maximal vertex degree higher than four [7]. No polynomial- time algorithm is known for computing a (region preserving) bend mini- mal Kandinsky drawing. In this paper we suggest a new 2-approximation algorithm for this problem. Our extensive computational experiments [13] show that the quality of the computed solutions is better than those of its predecessors [6]. E.g., for all instances in the Rome graph bench- mark library [4] it computed the optimal solution, and for randomly generated triangulated graphs with up to 800 vertices, the absolute error was less than 2 on average. 1 Introduction Given a planar graph G =(V,E,F) with a fixed embedding F ,weconsider the problem of finding a region-preserving planar orthogonal drawing with the minimum number of bends. For graphs with maximal degree 4 this problem can be solved in polynomial time [12] if no parallel edge segments leaving a vertex at the same side are allowed. The idea is to set up a network in which each flow corresponds to an orthogonal drawing and vice versa. -
Algorithms for Incremental Planar Graph Drawing and Two-Page Book Embeddings
Algorithms for Incremental Planar Graph Drawing and Two-page Book Embeddings Inaugural-Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakult¨at der Universit¨at zu Koln¨ vorgelegt von Martin Gronemann aus Unna Koln¨ 2015 Berichterstatter (Gutachter): Prof. Dr. Michael Junger¨ Prof. Dr. Markus Chimani Prof. Dr. Bettina Speckmann Tag der m¨undlichenPr¨ufung: 23. Juni 2015 Zusammenfassung Diese Arbeit besch¨aftigt sich mit zwei Problemen bei denen es um Knoten- ordnungen in planaren Graphen geht. Hierbei werden als erstes Ordnungen betrachtet, die als Grundlage fur¨ inkrementelle Zeichenalgorithmen dienen. Solche Algorithmen erweitern in der Regel eine vorhandene Zeichnung durch schrittweises Hinzufugen¨ von Knoten in der durch die Ordnung gegebene Rei- henfolge. Zu diesem Zweck kommen im Gebiet des Graphenzeichnens verschie- dene Ordnungstypen zum Einsatz. Einigen dieser Ordnungen fehlen allerdings gewunschte¨ oder sogar fur¨ einige Algorithmen notwendige Eigenschaften. Diese Eigenschaften werden genauer untersucht und dabei ein neuer Typ von Ord- nung entwickelt, die sogenannte bitonische st-Ordnung, eine Ordnung, welche Eigenschaften kanonischer Ordnungen mit der Flexibilit¨at herk¨ommlicher st- Ordnungen kombiniert. Die zus¨atzliche Eigenschaft bitonisch zu sein erm¨oglicht es, eine st-Ordnung wie eine kanonische Ordnung zu verwenden. Es wird gezeigt, dass fur¨ jeden zwei-zusammenh¨angenden planaren Graphen eine bitonische st-Ordnung in linearer Zeit berechnet werden kann. Im Ge- gensatz zu kanonischen Ordnungen, k¨onnen st-Ordnungen naturgem¨aß auch fur¨ gerichtete Graphen verwendet werden. Diese F¨ahigkeit ist fur¨ das Zeichnen von aufw¨artsplanaren Graphen von besonderem Interesse, da eine bitonische st-Ordnung unter Umst¨anden es erlauben wurde,¨ vorhandene ungerichtete Zei- chenverfahren fur¨ den gerichteten Fall anzupassen. -
New Approaches on Octilinear Graph Drawing
New Approaches on Octilinear Graph Drawing Dissertation der Mathematisch-Naturwissenschaftlichen Fakult¨at der Eberhard Karls Universit¨atT¨ubingen zur Erlangung des Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) vorgelegt von Dipl.-Inform. (Bioinf.) Robert Krug aus Ulm T¨ubingen 2015 abc Gedruckt mit Genehmigung der Mathematisch-Naturwissenschaftlichen Fakult¨at der Eberhard Karls Universit¨atT¨ubingen. Tag der m¨undlichen Qualifikation 28.04.2015 Dekan: Prof. Dr. Wolfgang Rosenstiel 1. Berichterstatter: Prof. Dr. Michael Kaufmann 2. Berichterstatter: Prof. Dr. Peter Hauck Acknowledgements First and foremost I thank my advisor Prof. Dr. Michael Kaufmann, for giving me the opportunity to be part of his great group and write this thesis, for supporting me this whole time and enabling me to visit several international workshops and conferences. I am deeply grateful to my colleague Dr. Michael Bekos who not only par- ticipated in every research project that made it into this thesis but also gave me great support during the writing of this thesis and was a very pleasant officemate. My thanks also go to my current and former colleagues Till Bruckdorfer, Philip Effinger, Andreas Gerasch, Niklas Heinsohn, Markus Geyer, Stephan Kottler, Martin Siebenhaller and Christian Zielke who provided a very nice working atmosphere and valuable input in many scientific and non-scientific discussions during our coffee-breaks. I thank all my coauthors, namely Michael Bekos, Markus Geyer, Martin Gronemann, Michael Kaufmann, Thorsten Ludwig, Stefan N¨aherand Vin- cenzo Roselli for the pleasant and productive collaborations. I am most thankful to my friends Chrissi and Klaus who, together with Ju- lian and Laura, always offered me delicious meals and delightful company which I especially appreciated in stressful times of approaching deadlines.