WEB-BASED DRAWING SOFTWARE for GRAPHS in 3D and TWO LAYOUT ALGORITHMS FARSHAD BARAHIMI Bachelor of Science, Shahid Bahonar Unive
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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. -
Review Article Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis
Hindawi Advances in Bioinformatics Volume 2017, Article ID 1278932, 8 pages https://doi.org/10.1155/2017/1278932 Review Article Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis Georgios A. Pavlopoulos,1 David Paez-Espino,1 Nikos C. Kyrpides,1 and Ioannis Iliopoulos2 1 Department of Energy, Joint Genome Institute, Lawrence Berkeley Labs, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA 2Division of Basic Sciences, University of Crete Medical School, Andrea Kalokerinou Street, Heraklion, Greece Correspondence should be addressed to Georgios A. Pavlopoulos; [email protected] and Ioannis Iliopoulos; [email protected] Received 22 February 2017; Revised 14 May 2017; Accepted 4 June 2017; Published 18 July 2017 Academic Editor: Klaus Jung Copyright © 2017 Georgios A. Pavlopoulos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today’s indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities. -
An XML-Based Description of Structured Networks
Massimo Ancona, Walter Cazzola, Sara Drago, and Francesco Guido. An XML-Based Description of Structured Networks. In Proceedings of Interna- tional Conference Communications 2004, pages 401–406, Bucharest, Romania, June 2004. IEEE Press. An XML-Based Description of Structured Networks Massimo Ancona£ Walter Cazzola† Sara Drago£ Francesco Guido‡ £ DISI University of Genova, e-mail: fancona,[email protected] † DICo University of Milano, e-mail: [email protected] ‡ Marconi Selenia Communication, e-mail: [email protected] Abstract In this paper we present an XML-based formalism to describe hierarchically organized communication networks. After a short overview of existing graph description languages, we discuss the advantages and disadvantages of their application in network optimization. We conclude by extending one of these formalisms with features supporting the description of the relationship between the optimized logical layout of a network and its physical counterpart. Elements for describing traffic parameters are also given. 1 Introduction The problem of network design and optimization has a strategical importance especially for military networks. In this case, design for fault tolerance and security plays a role more and more crucial than the equivalent importance required for commercial and research networks. The reengineering of a large communication network is a complex problem, which consists of different aspects that can be strongly affected by the way of describing data. The plainest way to describe a communication network is to model the relationship among sites and links by means of a weighted undirected graph, but unless some more assumptions are taken, this approach can raise several problems. A first issue is encountered when an analysis and a visualization of the network has to be realized: practical networks include hundreds or often thousands of nodes and links, so that even a simple description and documentation of the network structure is hard to maintain and update. -
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. -
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. -
Lesson 8 Graph Visual Analytics: Visualising and Analysing Network Data
ISSS608 Visual Analytics and Applications 10/14/2016 Lesson 8: Visualising and Analysing Network Data Lesson 8 Graph Visual Analytics: Visualising and Analysing Network Data Mentor: Dr. Kam Tin Seong Associate Professor of Information Systems (Practice) School of Information Systems, Singapore Management University Content • Introduction to Graph Visual Analytics • Graph Visualisation in Actions • Basic Principles of Graph – Network data sets – Graph data format • Network Visualisation and Analysis – Network visualisation and analysis process model – Graph layouts and visual attributes – Network metrics • Network visualisation and analysis tools – NodeXL – Gephi 2 8-1 ISSS608 Visual Analytics and Applications 10/14/2016 Lesson 8: Visualising and Analysing Network Data Network in real World • Physical – Transportation (i.e. road, port, rail, etc) – Utility (electricity, water, gas, network cable, etc) – Natural (river, etc) • Abstract – Social media (i.e. e-mail, Facebook, Twitter, Wikipedia, etc) – Organisation (i.e. NGO, politics, customer-company, staff-to-staff, criminal, terrorist, disease, etc) 3 Classical Graph Theory • The Seven Bridges of Königsberg is a historically notable problem in mathematics. Its negative resolution by Leonhard Euler in 1735 laid the foundations of graph theory and prefigured the idea of topology. Source: http://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg 4 8-2 ISSS608 Visual Analytics and Applications 10/14/2016 Lesson 8: Visualising and Analysing Network Data Classical Graph Visualisation and -
Lesson 8 Graph Visualisation and Analysis: Methods and Applications
IS428 Visual Analytics for Business Intelligence 10/8/2016 Lesson 8: Graph Visualisation and Analysis Lesson 8 Graph Visualisation and Analysis: Methods and Applications Mentor: Dr. Kam Tin Seong Associate Professor of Information Systems (Practice) School of Information Systems, Singapore Management University Content • Motivation of graph visualisation • Graph Visualisation in Actions • Typology of network data • Graph data file formats • Graph Layout Techniques • Network visualisation and analysis process model • Network Metrics • Network visualisation and analysis tools – Gephi – NodeXL 8-1 IS428 Visual Analytics for Business Intelligence 10/8/2016 Lesson 8: Graph Visualisation and Analysis Network in real World • Physical – Transportation (i.e. road, port, rail, etc) – Utility (electricity, water, gas, network cable, etc) – Natural (river, etc) • Abstract – Social media (i.e. e-mail, Facebook, Twitter, Wikipedia, etc) – Organisation (i.e. NGO, politics, customer-company, staff-to-staff, criminal, terrorist, disease, etc) 3 Classical Graph Theory • The Seven Bridges of Königsberg is a historically notable problem in mathematics. Its negative resolution by Leonhard Euler in 1735 laid the foundations of graph theory and prefigured the idea of topology. Source: http://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg 4 8-2 IS428 Visual Analytics for Business Intelligence 10/8/2016 Lesson 8: Graph Visualisation and Analysis Classical Graph Visualisation and Analysis • Using sociogram, also know as network graph Early 20th-century -
Practical Experiments in Graph Based Testing
Practical Experiences in Graph-Based Testing Alan Richardson Practical Experiences in Graph-Based Testing ABSTRACT Herein you are introduced to different structural diagramming techniques, but primarily the generic directed graph. Graph usage is explained as part of a structured testing process and during exploratory testing. The thought processes and models of testing which have led to my usage of graphs are examined. I will also list, review and comment on free and inexpensive tools which you can use to incorporate graph based tests into your testing process. Author’s Biography While working as a developer, coding software testing tools, Alan Richardson’s interest switched from programming to software testing. Since 1993, software testing has been Alan’s professional specialism and he has worked at all levels of the testing hierarchy; test execution and design, test management, strategy and methodology. He is currently an independent test consultant and helps his clients with every aspect of software testing. Alan holds a BSc in Computing (Hons), and the ISEB foundation certificate in software testing. Alan can be found on the ISEB examination markers panel for Software Testing, and is a member of the BCS London branch of the Special Interest Group in Software Testing. He manages and maintains a web site dedicated to software testing (http://www.compendiumdev.co.uk) and has written a number of freely downloadable tools to help in the testing process. When not being paid to test, Alan is generally reading about testing, beta testing useful -
Simulation in Metabolic Networks
Die approbierte Originalversion dieser Dissertation ist in der Hauptbibliothek der Technischen Universität Wien aufgestellt und zugänglich. http://www.ub.tuwien.ac.at The approved original version of this thesis is available at the main library of the Vienna University of Technology. http://www.ub.tuwien.ac.at/eng Simulation in Metabolic Networks DISSERTATION zur Erlangung des akademischen Grades Doktor der technischen Wissenschaften eingereicht von Christian Siehs Matrikelnummer 9203648 an der Fakultät für Informatik der Technischen Universität Wien Betreuung: Ao. Univ.-Prof. Dr. Rudolf Freund Diese Dissertation haben begutachtet:: _______________________ ________________________ (Ao. Univ.-Prof. Dr. Rudolf Freund) (Univ.Doz. Dr. Bernd Mayer) _______________________ Wien, 28.09.2015 (Christian Siehs) Technische Universität Wien A-1040 ▪ Karlsplatz 13 ▪ Tel. +43-1-58801-01 ▪ www.tuwien.ac.at Simulation in Metabolic Networks DISSERTATION submitted in partial fulfillment of the requirements for the degree of Doktor der technischen Wissenschaften by Christian Siehs Registration Number 9203648 to the Faculty of Informatics at the Vienna University of Technology Advisor: Ao. Univ.-Prof. Dr. Rudolf Freund The dissertation has been reviewed by: _______________________ ________________________ (Ao. Univ.-Prof. Dr. Rudolf Freund) (Univ.Doz. Dr. Bernd Mayer) _______________________ Wien, 28.09.2015 (Christian Siehs) Technische Universität Wien A-1040 ▪ Karlsplatz 13 ▪ Tel. +43-1-58801-01 ▪ www.tuwien.ac.at Erklärung zur Verfassung der Arbeit Christian Siehs Keinergasse 20/1/12, 1030 Wien Hiermit erkläre ich, dass ich diese Arbeit selbständig verfasst habe, dass ich die verwendeten Quellen und Hilfsmittel vollständig angegeben habe und dass ich die Stellen der Arbeit - einschließlich Tabellen, Karten und Abbildungen -, die anderen Werken oder dem Internet im Wortlaut oder dem Sinn nach entnommen sind, auf jeden Fall unter Angabe der Quelle als Entlehnung kenntlich gemacht habe. -
TCML – an XML-Based Test Case Format
TCML – An XML-Based Test Case Format Mohd Farid Jaafar, Mohd Hasan Selamat, Abdul Azim Abdul Ghani Faculty of Computer Science and Information Technology University Putra Malaysia 43400 UPM Serdang Selangor, MALAYSIA {mfarid, hasan, azim}@fsktm.upm.edu.my Abstract - Testing is important and gives the confident software [2, 3]. This cost is just for a normal to the developer and user; but it is expensive and may application but if it involves extreme and critical consume at least 50% of the total costs involved in software, the number might increases. The main developing software. This cost is just for a normal concern in testing is the effectiveness of finding an application but if it involves extreme and critical error. If it doesn’t find any error, it can be considered software, the number might increases. As we know, designing test suite for testing is a tedious and as failed. This can be avoided by careful design of meticulous work. We need to have a deep test case and good selection of test suite. understanding of the program before we could design a good test suite and even an expert tends to miss some Most studies in designing test cases are using test cases. Manual testing for large-scale program is requirement specification, complex technique i.e. Z very expensive and frustrating for humans. The same and third-party tools that require novices and non- scenario applies in Visual Programming Language expert user to supply information that are not (VPL). Visual Programming (VP) is a much complex familiar to them. -
To Medium-Scale Gene Regulatory Networks
GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks Kam D. Dahlquist1, John David N. Dionisio2, Ben G. Fitzpatrick3, Nicole A. Anguiano2, Anindita Varshneya1, Britain J. Southwick2 and Mihir Samdarshi1 1 Department of Biology, Loyola Marymount University, Los Angeles, California, United States 2 Department of Electrical Engineering and Computer Science, Loyola Marymount University, Los Angeles, California, United States 3 Department of Mathematics, Loyola Marymount University, Los Angeles, California, United States ABSTRACT GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. When a user uploads an input file specifying an unweighted network, GRNsight automatically lays out the graph using black lines and pointed arrowheads. For a weighted network, GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign Submitted 21 May 2016 (positive for activation or negative for repression) and magnitude of the weight Accepted 20 August 2016 parameter.