Graphml Transformation
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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 -
Fcastone - FCA file Format Conversion and Interoperability Software
FcaStone - FCA file format conversion and interoperability software Uta Priss Napier University, School of Computing, [email protected] www.upriss.org.uk Abstract. This paper presents FcaStone - a software for FCA file format con- version and interoperability. The paper both describes the features of FcaStone and discusses FCA interoperability issues (such as the specific difficulties en- countered when connecting FCA tools to other tools). The paper ends with a call to the FCA community to develop some sort of standard FCA Interchange For- mat, which could be used for data exchange between stand-alone applications and across the web. 1 Introduction FcaStone1 (named in analogy to ”Rosetta Stone”) is a command-line utility that con- verts between the file formats of commonly-used FCA2 tools (such as ToscanaJ, Con- Exp, Galicia, Colibri3) and between FCA formats and other graph and vector graph- ics formats. The main purpose of FcaStone is to improve the interoperability between FCA, graph editing and vector graphics software. Because it is a command-line tool, FcaStone can easily be incorporated into server-side web applications, which generate concept lattices on demand. FcaStone is open source software and available for down- load from Sourceforge. FcaStone is written in an interpreted language (Perl) and thus platform-independent. Installation on Linux and Apple OS X consists of copying the file to a suitable location. Installation on Windows has also been tested and is also fairly easy. The need for interoperability software was highlighted in discussions among ICCS participants in recent years. Several ICCS authors expressed disappointment with the lack of progress in conceptual structures (CS) research with respect to applications and software (Chein & Genest (2000); Keeler & Pfeiffer (2006)) and with respect to current web developments (Rudolph et al., 2007). -
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 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 -
Chapter 18 Spectral Graph Drawing
Chapter 18 Spectral Graph Drawing 18.1 Graph Drawing and Energy Minimization Let G =(V,E)besomeundirectedgraph.Itisoftende- sirable to draw a graph, usually in the plane but possibly in 3D, and it turns out that the graph Laplacian can be used to design surprisingly good methods. n Say V = m.Theideaistoassignapoint⇢(vi)inR to the vertex| | v V ,foreveryv V ,andtodrawaline i 2 i 2 segment between the points ⇢(vi)and⇢(vj). Thus, a graph drawing is a function ⇢: V Rn. ! 821 822 CHAPTER 18. SPECTRAL GRAPH DRAWING We define the matrix of a graph drawing ⇢ (in Rn) as a m n matrix R whose ith row consists of the row vector ⇥ n ⇢(vi)correspondingtothepointrepresentingvi in R . Typically, we want n<m;infactn should be much smaller than m. Arepresentationisbalanced i↵the sum of the entries of every column is zero, that is, 1>R =0. If a representation is not balanced, it can be made bal- anced by a suitable translation. We may also assume that the columns of R are linearly independent, since any basis of the column space also determines the drawing. Thus, from now on, we may assume that n m. 18.1. GRAPH DRAWING AND ENERGY MINIMIZATION 823 Remark: Agraphdrawing⇢: V Rn is not required to be injective, which may result in! degenerate drawings where distinct vertices are drawn as the same point. For this reason, we prefer not to use the terminology graph embedding,whichisoftenusedintheliterature. This is because in di↵erential geometry, an embedding always refers to an injective map. The term graph immersion would be more appropriate. -
Graph Drawing by Stochastic Gradient Descent
1 Graph Drawing by Stochastic Gradient Descent Jonathan X. Zheng, Samraat Pawar, Dan F. M. Goodman Abstract—A popular method of force-directed graph drawing is multidimensional scaling using graph-theoretic distances as input. We present an algorithm to minimize its energy function, known as stress, by using stochastic gradient descent (SGD) to move a single pair of vertices at a time. Our results show that SGD can reach lower stress levels faster and more consistently than majorization, without needing help from a good initialization. We then show how the unique properties of SGD make it easier to produce constrained layouts than previous approaches. We also show how SGD can be directly applied within the sparse stress approximation of Ortmann et al. [1], making the algorithm scalable up to large graphs. Index Terms—Graph drawing, multidimensional scaling, constraints, relaxation, stochastic gradient descent F 1 INTRODUCTION RAPHS are a common data structure, used to describe to the shortest path distance between vertices i and j, with w = d−2 G everything from social networks to food webs, from ij ij to offset the extra weight given to longer paths metabolic pathways to internet traffic. Any set of pairwise due to squaring the difference [3]. relationships between entities can be described by a graph, This definition was popularized for graph layout by and the ever increasing amount of data being collected Kamada and Kawai [4] who minimized the function using means that visualizing graphs for exploratory analysis has a localized 2D Newton-Raphson method, while within the become an important task. MDS community Kruskal [5] originally used gradient de- Node-link diagrams are an intuitive representation of scent [6]. -
The Hitchhiker's Guide to Graph Exchange Formats
The Hitchhiker’s Guide to Graph Exchange Formats Prof. Matthew Roughan [email protected] http://www.maths.adelaide.edu.au/matthew.roughan/ Work with Jono Tuke UoA June 4, 2015 M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 1 / 31 Graphs Graph: G(N; E) I N = set of nodes (vertices) I E = set of edges (links) Often we have additional information, e.g., I link distance I node type I graph name M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 2 / 31 Why? To represent data where “connections” are 1st class objects in their own right I storing the data in the right format improves access, processing, ... I it’s natural, elegant, efficient, ... Many, many datasets M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 3 / 31 ISPs: Internode: layer 3 http: //www.internode.on.net/pdf/network/internode-domestic-ip-network.pdf M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 4 / 31 ISPs: Level 3 (NA) http://www.fiberco.org/images/Level3-Metro-Fiber-Map4.jpg M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 5 / 31 Telegraph submarine cables http://en.wikipedia.org/wiki/File:1901_Eastern_Telegraph_cables.png M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 6 / 31 Electricity grid M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 7 / 31 Bus network (Adelaide CBD) M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 8 / 31 French Rail http://www.alleuroperail.com/europe-map-railways.htm M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 9 / 31 Protocol relationships M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 10 / 31 Food web M.Roughan (UoA) Hitch Hikers -
A DSL for Defining Instance Templates for the ASMIG System
A DSL for defining instance templates for the ASMIG system Andrii Kovalov Dissertation 2014 Erasmus Mundus MSc in Dependable Software Systems Department of Computer Science National University of Ireland, Maynooth Co. Kildare, Ireland A dissertation submitted in partial fulfilment of the requirements for the Erasmus Mundus MSc Dependable Software Systems Head of Department : Dr Adam Winstanley Supervisor : Dr James Power Date: June 2014 Abstract The area of our work is test data generation via automatic instantiation of software models. Model instantiation or model finding is a process of find- ing instances of software models. For example, if a model is represented as a UML class diagram, the instances of this model are UML object diagrams. Model instantiation has several applications: finding solutions to problems expressed as models, model testing and test data generation. There are sys- tems that automatically generate model instances, one of them is ASMIG (A Small Metamodel Instance Generator). This system is focused on a `problem solving' use case. The motivation of our work is to adapt ASMIG system for use as a test data generator and make the instance generation process more transparent for the user. In order to achieve this we provided a way for the user to interact with ASMIG internal data structure, the instance template graph via a specially designed graph definition domain-specific language. As a result, the user is able to configure the instance template in order to get plausible instances, which can be then used as test data. Although model finding is only suitable for obtaining test inputs, but not the expected test outputs, it can be applied effectively for smoke testing of systems that process complex hierarchic data structures such as programming language parsers. -
Comm 645 Handout – Nodexl Basics
COMM 645 HANDOUT – NODEXL BASICS NodeXL: Network Overview, Discovery and Exploration for Excel. Download from nodexl.codeplex.com Plugin for social media/Facebook import: socialnetimporter.codeplex.com Plugin for Microsoft Exchange import: exchangespigot.codeplex.com Plugin for Voson hyperlink network import: voson.anu.edu.au/node/13#VOSON-NodeXL Note that NodeXL requires MS Office 2007 or 2010. If your system does not support those (or you do not have them installed), try using one of the computers in the PhD office. Major sections within NodeXL: • Edges Tab: Edge list (Vertex 1 = source, Vertex 2 = destination) and attributes (Fig.1→1a) • Vertices Tab: Nodes and attribute (nodes can be imported from the edge list) (Fig.1→1b) • Groups Tab: Groups of nodes defined by attribute, clusters, or components (Fig.1→1c) • Groups Vertices Tab: Nodes belonging to each group (Fig.1→1d) • Overall Metrics Tab: Network and node measures & graphs (Fig.1→1e) Figure 1: The NodeXL Interface 3 6 8 2 7 9 13 14 5 12 4 10 11 1 1a 1b 1c 1d 1e Download more network handouts at www.kateto.net / www.ognyanova.net 1 After you install the NodeXL template, a new NodeXL tab will appear in your Excel interface. The following features will be available in it: Fig.1 → 1: Switch between different data tabs. The most important two tabs are "Edges" and "Vertices". Fig.1 → 2: Import data into NodeXL. The formats you can use include GraphML, UCINET DL files, and Pajek .net files, among others. You can also import data from social media: Flickr, YouTube, Twitter, Facebook (requires a plugin), or a hyperlink networks (requires a plugin). -
Networkx: Network Analysis with Python
NetworkX: Network Analysis with Python Dionysis Manousakas (special thanks to Petko Georgiev, Tassos Noulas and Salvatore Scellato) Social and Technological Network Data Analytics Computer Laboratory, University of Cambridge February 2017 Outline 1. Introduction to NetworkX 2. Getting started with Python and NetworkX 3. Basic network analysis 4. Writing your own code 5. Ready for your own analysis! 2 1. Introduction to NetworkX 3 Introduction: networks are everywhere… Social Mobile phone networks Vehicular flows networks Web pages/citations Internet routing How can we analyse these networks? Python + NetworkX 4 Introduction: why Python? Python is an interpreted, general-purpose high-level programming language whose design philosophy emphasises code readability + - Clear syntax, indentation Can be slow Multiple programming paradigms Beware when you are Dynamic typing, late binding analysing very large Multiple data types and structures networks Strong on-line community Rich documentation Numerous libraries Expressive features Fast prototyping 5 Introduction: Python’s Holy Trinity Python’s primary NumPy is an extension to Matplotlib is the library for include primary plotting mathematical and multidimensional arrays library in Python. statistical computing. and matrices. Contains toolboxes Supports 2-D and 3-D for: Both SciPy and NumPy plotting. All plots are • Numeric rely on the C library highly customisable optimization LAPACK for very fast and ready for implementation. professional • Signal processing publication. • Statistics, and more…