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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. -
2 Graphs and Graph Theory
2 Graphs and Graph Theory chapter:graphs Graphs are the mathematical objects used to represent networks, and graph theory is the branch of mathematics that involves the study of graphs. Graph theory has a long history. The notion of graph was introduced for the first time in 1763 by Euler, to settle a famous unsolved problem of his days, the so-called “K¨onigsberg bridges” problem. It is no coin- cidence that the first paper on graph theory arose from the need to solve a problem from the real world. Also subsequent works in graph theory by Kirchhoff and Cayley had their root in the physical world. For instance, Kirchhoff’s investigations on electric circuits led to his development of a set of basic concepts and theorems concerning trees in graphs. Nowadays, graph theory is a well established discipline which is commonly used in areas as diverse as computer science, sociology, and biology. To make some examples, graph theory helps us to schedule airplane routings, and has solved problems such as finding the maximum flow per unit time from a source to a sink in a network of pipes, or coloring the regions of a map using the minimum number of different colors so that no neighbouring regions are colored the same way. In this chapter we introduce the basic definitions, set- ting up the language we will need in the following of the book. The two last sections are respectively devoted to the proof of the Euler theorem, and to the description of a graph as an array of numbers. -
Data Structures and Network Algorithms [Tarjan 1987-01-01].Pdf
CBMS-NSF REGIONAL CONFERENCE SERIES IN APPLIED MATHEMATICS A series of lectures on topics of current research interest in applied mathematics under the direction of the Conference Board of the Mathematical Sciences, supported by the National Science Foundation and published by SIAM. GAKRHT BiRKiion , The Numerical Solution of Elliptic Equations D. V. LINDIY, Bayesian Statistics, A Review R S. VAR<;A. Functional Analysis and Approximation Theory in Numerical Analysis R R H:\II\DI:R, Some Limit Theorems in Statistics PXIKK K Bin I.VISLI -y. Weak Convergence of Measures: Applications in Probability .1. I.. LIONS. Some Aspects of the Optimal Control of Distributed Parameter Systems R(H;I:R PI-NROSI-:. Tecltniques of Differentia/ Topology in Relativity Hi.KM \N C'ui KNOI r. Sequential Analysis and Optimal Design .1. DI'KHIN. Distribution Theory for Tests Based on the Sample Distribution Function Soi I. Ri BINO\\, Mathematical Problems in the Biological Sciences P. D. L\x. Hyperbolic Systems of Conservation Laws and the Mathematical Theory of Shock Waves I. .1. Soioi.NUiiRci. Cardinal Spline Interpolation \\.\\ SiMii.R. The Theory of Best Approximation and Functional Analysis WI-.KNI R C. RHHINBOLDT, Methods of Solving Systems of Nonlinear Equations HANS I-'. WHINBKRQKR, Variational Methods for Eigenvalue Approximation R. TYRRM.I. ROCKAI-KLI.AK, Conjugate Dtialitv and Optimization SIR JAMKS LIGHTHILL, Mathematical Biofhtiddynamics GI-.RAKD SAI.ION, Theory of Indexing C \ rnLi-:i;.N S. MORAWKTX, Notes on Time Decay and Scattering for Some Hyperbolic Problems F. Hoi'i'hNSTKAm, Mathematical Theories of Populations: Demographics, Genetics and Epidemics RK HARD ASKF;Y. -
Graphs Introduction and Depth-First Algorithm Carol Zander
Graphs Introduction and Depth‐first algorithm Carol Zander Introduction to graphs Graphs are extremely common in computer science applications because graphs are common in the physical world. Everywhere you look, you see a graph. Intuitively, a graph is a set of locations and edges connecting them. A simple example would be cities on a map that are connected by roads. Or cities connected by airplane routes. Another example would be computers in a local network that are connected to each other directly. Constellations of stars (among many other applications) can be also represented this way. Relationships can be represented as graphs. Section 9.1 has many good graph examples. Graphs can be viewed in three ways (trees, too, since they are special kind of graph): 1. A mathematical construction – this is how we will define them 2. An abstract data type – this is how we will think about interfacing with them 3. A data structure – this is how we will implement them The mathematical construction gives the definition of a graph: graph G = (V, E) consists of a set of vertices V (often called nodes) and a set of edges E (sometimes called arcs) that connect the edges. Each edge is a pair (u, v), such that u,v ∈V . Every tree is a graph, but not vice versa. There are two types of graphs, directed and undirected. In a directed graph, the edges are ordered pairs, for example (u,v), indicating that a path exists from u to v (but not vice versa, unless there is another edge.) For the edge, (u,v), v is said to be adjacent to u, but not the other way, i.e., u is not adjacent to v. -
Package 'Igraph'
Package ‘igraph’ February 28, 2013 Version 0.6.5-1 Date 2013-02-27 Title Network analysis and visualization Author See AUTHORS file. Maintainer Gabor Csardi <[email protected]> Description Routines for simple graphs and network analysis. igraph can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization,centrality indices and much more. Depends stats Imports Matrix Suggests igraphdata, stats4, rgl, tcltk, graph, Matrix, ape, XML,jpeg, png License GPL (>= 2) URL http://igraph.sourceforge.net SystemRequirements gmp, libxml2 NeedsCompilation yes Repository CRAN Date/Publication 2013-02-28 07:57:40 R topics documented: igraph-package . .5 aging.prefatt.game . .8 alpha.centrality . 10 arpack . 11 articulation.points . 15 as.directed . 16 1 2 R topics documented: as.igraph . 18 assortativity . 19 attributes . 21 autocurve.edges . 23 barabasi.game . 24 betweenness . 26 biconnected.components . 28 bipartite.mapping . 29 bipartite.projection . 31 bonpow . 32 canonical.permutation . 34 centralization . 36 cliques . 39 closeness . 40 clusters . 42 cocitation . 43 cohesive.blocks . 44 Combining attributes . 48 communities . 51 community.to.membership . 55 compare.communities . 56 components . 57 constraint . 58 contract.vertices . 59 conversion . 60 conversion between igraph and graphNEL graphs . 62 convex.hull . 63 decompose.graph . 64 degree . 65 degree.sequence.game . 66 dendPlot . 67 dendPlot.communities . 68 dendPlot.igraphHRG . 70 diameter . 72 dominator.tree . 73 Drawing graphs . 74 dyad.census . 80 eccentricity . 81 edge.betweenness.community . 82 edge.connectivity . 84 erdos.renyi.game . 86 evcent . 87 fastgreedy.community . 89 forest.fire.game . 90 get.adjlist . 92 get.edge.ids . 93 get.incidence . 94 get.stochastic . -
Py4cytoscape Documentation Release 0.0.1
py4cytoscape Documentation Release 0.0.1 The Cytoscape Consortium Jun 21, 2020 CONTENTS 1 Audience 3 2 Python 5 3 Free Software 7 4 History 9 5 Documentation 11 6 Indices and tables 271 Python Module Index 273 Index 275 i ii py4cytoscape Documentation, Release 0.0.1 py4cytoscape is a Python package that communicates with Cytoscape via its REST API, providing access to a set over 250 functions that enable control of Cytoscape from within standalone and Notebook Python programming environ- ments. It provides nearly identical functionality to RCy3, an R package in Bioconductor available to R programmers. py4cytoscape provides: • functions that can be leveraged from Python code to implement network biology-oriented workflows; • access to user-written Cytoscape Apps that implement Cytoscape Automation protocols; • logging and debugging facilities that enable rapid development, maintenance, and auditing of Python-based workflow; • two-way conversion between the igraph and NetworkX graph packages, which enables interoperability with popular packages available in public repositories (e.g., PyPI); and • the ability to painlessly work with large data sets generated within Python or available on public repositories (e.g., STRING and NDEx). With py4cytoscape, you can leverage Cytoscape to: • load and store networks in standard and nonstandard data formats; • visualize molecular interaction networks and biological pathways; • integrate these networks with annotations, gene expression profiles and other state data; • analyze, profile, and cluster these networks based on integrated data, using new and existing algorithms. py4cytoscape enables an agile collaboration between powerful Cytoscape, Python libraries, and novel Python code so as to realize auditable, reproducible and sharable workflows. -
Bioinformatics Methods for the Genetic and Molecular Characterisation Of
BIOINFORMATICS METHODSFORTHE GENETICAND MOLECULAR CHARACTERISATION OFCANCER Dissertation zur Erlangung des Grades des Doktors der Naturwissenschaften der Naturwissenschaftlich-Technischen Fakultäten der Universität des Saarlandes vo n DANIELSTÖCKEL Universität des Saarlandes, MI – Fakultät für Mathematik und Informatik, Informatik b e t r e u e r PROF.DR.HANS-PETERLENHOF 6. dezember 2016 Daten der Verteidigung Datum 25.11.2016, 15 Uhr ct. Vorsitzender Prof. Dr. Volkhard Helms Erstgutachter Prof. Dr. Hans-Peter Lenhof Zweitgutachter Prof. Dr. Andreas Keller Beisitzerin Dr. Christina Backes Dekan Prof. Dr. Frank-Olaf Schreyer This thesis is also available online at: https://somweyr.de/~daniel/ dissertation.zip ii a b s t r ac t Cancer is a class of complex, heterogeneous diseases of which many types have proven to be difficult to treat due to the high ge- netic variability between and within tumours. To improve ther- apy, some cases require a thorough genetic and molecular charac- terisation that allows to identify mutations and pathogenic pro- cesses playing a central role for the development of the disease. Data obtained from modern, biological high-throughput exper- iments can offer valuable insights in this regard. Therefore, we developed a range of interoperable approaches that support the analysis of high-throughput datasets on multiple levels of detail. Mutations are a main driving force behind the development of cancer. To assess their impact on an affected protein, we de- signed BALL-SNP which allows to visualise and analyse single nucleotide variants in a structure context. For modelling the ef- fect of mutations on biological processes we created CausalTrail which is based on causal Bayesian networks and the do-calculus. -
Chapter 12 Graphs and Their Representation
Chapter 12 Graphs and their Representation 12.1 Graphs and Relations Graphs (sometimes referred to as networks) offer a way of expressing relationships between pairs of items, and are one of the most important abstractions in computer science. Question 12.1. What makes graphs so special? What makes graphs special is that they represent relationships. As you will (or might have) discover (discovered already) relationships between things from the most abstract to the most concrete, e.g., mathematical objects, things, events, people are what makes everything inter- esting. Considered in isolation, hardly anything is interesting. For example, considered in isolation, there would be nothing interesting about a person. It is only when you start consid- ering his or her relationships to the world around, the person becomes interesting. Challenge yourself to try to find something interesting about a person in isolation. You will have difficulty. Even at a biological level, what is interesting are the relationships between cells, molecules, and the biological mechanisms. Question 12.2. Trees captures relationships too, so why are graphs more interesting? Graphs are more interesting than other abstractions such as tree, which can also represent certain relationships, because graphs are more expressive. For example, in a tree, there cannot be cycles, and multiple paths between two nodes. Question 12.3. What do we mean by a “relationship”? Can you think of a mathematical way to represent relationships. 231 232 CHAPTER 12. GRAPHS AND THEIR REPRESENTATION Alice Bob Josefa Michel Figure 12.1: Friendship relation f(Alice, Bob), (Bob, Alice), (Bob, Michel), (Michel, Bob), (Josefa, Michel), (Michel, Josefa)g as a graph. -
Compiler Support for Operator Overloading and Algorithmic Differentiation in C++
Compiler Support for Operator Overloading and Algorithmic Differentiation in C++ Zur Erlangung des Grades eines Doktors der Naturwissenschaften (Dr.rer.nat.) genehmigte Dissertation von Alexander Hück, M.Sc. aus Wiesbaden Tag der Einreichung: 16.01.2020, Tag der Prüfung: 28.02.2020 1. Gutachten: Prof. Dr. Christian Bischof 2. Gutachten: Prof. Dr. Martin Bücker Darmstadt – D 17 Computer Science Department Scientific Computing Compiler Support for Operator Overloading and Algorithmic Differentiation in C++ Doctoral thesis by Alexander Hück, M.Sc. 1. Review: Prof. Dr. Christian Bischof 2. Review: Prof. Dr. Martin Bücker Date of submission: 16.01.2020 Date of thesis defense: 28.02.2020 Darmstadt – D 17 Bitte zitieren Sie dieses Dokument als: URN: urn:nbn:de:tuda-tuprints-115226 URL: http://tuprints.ulb.tu-darmstadt.de/11522 Dieses Dokument wird bereitgestellt von tuprints, E-Publishing-Service der TU Darmstadt http://tuprints.ulb.tu-darmstadt.de [email protected] Die Veröffentlichung steht unter folgender Creative Commons Lizenz: Namensnennung – Keine Bearbeitungen 4.0 International (CC BY–ND 4.0) The publication is under the following Creative Commons license: Attribution – NoDerivatives 4.0 International (CC BY–ND 4.0) Erklärungen laut Promotionsordnung §8 Abs. 1 lit. c PromO Ich versichere hiermit, dass die elektronische Version meiner Dissertation mit der schriftli- chen Version übereinstimmt. §8 Abs. 1 lit. d PromO Ich versichere hiermit, dass zu einem vorherigen Zeitpunkt noch keine Promotion versucht wurde. In diesem Fall sind nähere Angaben über Zeitpunkt, Hochschule, Dissertationsthe- ma und Ergebnis dieses Versuchs mitzuteilen. §9 Abs. 1 PromO Ich versichere hiermit, dass die vorliegende Dissertation selbstständig und nur unter Verwendung der angegebenen Quellen verfasst wurde. -
A Brief Study of Graph Data Structure
ISSN (Online) 2278-1021 IJARCCE ISSN (Print) 2319 5940 International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 6, June 2016 A Brief Study of Graph Data Structure Jayesh Kudase1, Priyanka Bane2 B.E. Student, Dept of Computer Engineering, Fr. Conceicao Rodrigues College of Engineering, Maharashtra, India1, 2 Abstract: Graphs are a fundamental data structure in the world of programming. Graphs are used as a mean to store and analyse metadata. A graph implementation needs understanding of some of the common basic fundamentals of graph. This paper provides a brief study of graph data structure. Various representations of graph and its traversal techniques are discussed in the paper. An overview to the advantages, disadvantages and applications of the graphs is also provided. Keywords: Graph, Vertices, Edges, Directed, Undirected, Adjacency, Complexity. I. INTRODUCTION Graph is a data structure that consists of finite set of From the graph shown in Fig. 1, we can write that vertices, together with a set of unordered pairs of these V = {a, b, c, d, e} vertices for an undirected graph or a set of ordered pairs E = {ab, ac, bd, cd, de} for a directed graph. These pairs are known as Adjacency relation: Node B is adjacent to A if there is an edges/lines/arcs for undirected graphs and directed edge / edge from A to B. directed arc / directed line for directed graphs. An edge Paths and reachability: A path from A to B is a sequence can be associated with some edge value such as a numeric of vertices A1… A such that there is an edge from A to attribute. -
A Tool for Large Scale Network Analysis, Modeling and Visualization
A Tool For Large Scale Network Analysis, Modeling and Visualization Weixia (Bonnie) Huang Cyberinfrastructure for Network Science Center School of Library and Information Science Indiana University, Bloomington, IN Network Workbench ( http://nwb.slis.indiana.edu ). 1 Project Details Investigators: Katy Börner, Albert-Laszlo Barabasi, Santiago Schnell, Alessandro Vespignani & Stanley Wasserman, Eric Wernert Software Team: Lead: Weixia (Bonnie) Huang Developers: Santo Fortunato, Russell Duhon, Bruce Herr, Tim Kelley, Micah Walter Linnemeier, Megha Ramawat, Ben Markines, M Felix Terkhorn, Ramya Sabbineni, Vivek S. Thakre, & Cesar Hidalgo Goal: Develop a large-scale network analysis, modeling and visualization toolkit for physics, biomedical, and social science research. Amount: $1,120,926, NSF IIS-0513650 award Duration: Sept. 2005 - Aug. 2008 Website: http://nwb.slis.indiana.edu Network Workbench ( http://nwb.slis.indiana.edu ). 2 Project Details (cont.) NWB Advisory Board: James Hendler (Semantic Web) http://www.cs.umd.edu/~hendler/ Jason Leigh (CI) http://www.evl.uic.edu/spiff/ Neo Martinez (Biology) http://online.sfsu.edu/~webhead/ Michael Macy, Cornell University (Sociology) http://www.soc.cornell.edu/faculty/macy.shtml Ulrik Brandes (Graph Theory) http://www.inf.uni-konstanz.de/~brandes/ Mark Gerstein, Yale University (Bioinformatics) http://bioinfo.mbb.yale.edu/ Stephen North (AT&T) http://public.research.att.com/viewPage.cfm?PageID=81 Tom Snijders, University of Groningen http://stat.gamma.rug.nl/snijders/ Noshir Contractor, Northwestern University http://www.spcomm.uiuc.edu/nosh/ Network Workbench ( http://nwb.slis.indiana.edu ). 3 Outline What is “Network Science” and its challenges Major contributions of Network Workbench (NWB) Present the underlying technologies – NWB tool architecture Hand on NWB tool Review some large scale network analysis and visualization works Network Workbench ( http://nwb.slis.indiana.edu ).