Recent Large Graph Visualization Tools : Areview
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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 -
STATS 701 Data Analysis Using Python Lecture 27: Apis and Graph Processing Some Slides Adapted from C
STATS 701 Data Analysis using Python Lecture 27: APIs and Graph Processing Some slides adapted from C. Budak Previously: Scraping Data from the Web We used BeautifulSoup to process HTML that we read directly We had to figure out where to find the data in the HTML This was okay for simple things like Wikipedia… ...but what about large, complicated data sets? E.g., Climate data from NOAA; Twitter/reddit/etc.; Google maps Many websites support APIs, which make these tasks simpler Instead of scraping for what we want, just ask! Example: ask Google Maps for a computer repair shop near a given address Three common API approaches Via a Python package Service (e.g., Google maps, ESRI*) provides library for querying DB Example: from arcgis.gis import GIS Via a command-line tool Ultimately, all three of these approaches end up submitting an Example: twurl https://developer.twitter.com/ HTTP request to a server, which returns information in the form of a Via HTTP requests JSON or XML file, typically. We submit an HTTP request to a server Supply additional parameters in URL to specify our query Example: https://www.yelp.com/developers/documentation/v3/business_search * ESRI is a GIS service, to which the university has a subscription: https://developers.arcgis.com/python/ Web service APIs Step 1: Create URL with query parameters Example (non-working): www.example.com/search?key1=val1&key2=val2 Step 2: Make an HTTP request Communicates to the server what kind of action we wish to perform https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol#Request_methods Step 3: Server returns a response to your request May be as simple as a code (e.g., 404 error).. -
Gephi Tools for Network Analysis and Visualization
Frontiers of Network Science Fall 2018 Class 8: Introduction to Gephi Tools for network analysis and visualization Boleslaw Szymanski CLASS PLAN Main Topics • Overview of tools for network analysis and visualization • Installing and using Gephi • Gephi hands-on labs Frontiers of Network Science: Introduction to Gephi 2018 2 TOOLS OVERVIEW (LISTED ALPHABETICALLY) Tools for network analysis and visualization • Computing model and interface – Desktop GUI applications – API/code libraries, Web services – Web GUI front-ends (cloud, distributed, HPC) • Extensibility model – Only by the original developers – By other users/developers (add-ins, modules, additional packages, etc.) • Source availability model – Open-source – Closed-source • Business model – Free of charge – Commercial Frontiers of Network Science: Introduction to Gephi 2018 3 TOOLS CINET CyberInfrastructure for NETwork science • Accessed via a Web-based portal (http://cinet.vbi.vt.edu/granite/granite.html) • Supported by grants, no charge for end users • Aims to provide researchers, analysts, and educators interested in Network Science with an easy-to-use cyber-environment that is accessible from their desktop and integrates into their daily work • Users can contribute new networks, data, algorithms, hardware, and research results • Primarily for research, teaching, and collaboration • No programming experience is required Frontiers of Network Science: Introduction to Gephi 2018 4 TOOLS Cytoscape Network Data Integration, Analysis, and Visualization • A standalone GUI application -
Fastnet: an R Package for Fast Simulation and Analysis of Large-Scale Social Networks
JSS Journal of Statistical Software November 2020, Volume 96, Issue 7. doi: 10.18637/jss.v096.i07 fastnet: An R Package for Fast Simulation and Analysis of Large-Scale Social Networks Xu Dong Luis Castro Nazrul Shaikh Tamr Inc. World Bank Cecareus Inc. Abstract Traditional tools and software for social network analysis are seldom scalable and/or fast. This paper provides an overview of an R package called fastnet, a tool for scaling and speeding up the simulation and analysis of large-scale social networks. fastnet uses multi-core processing and sub-graph sampling algorithms to achieve the desired scale-up and speed-up. Simple examples, usages, and comparisons of scale-up and speed-up as compared to other R packages, i.e., igraph and statnet, are presented. Keywords: social network analysis, network simulation, network metrics, multi-core process- ing, sampling. 1. Introduction It has been about twenty years since the introduction of social network analysis (SNA) soft- ware such as Pajek (Batagelj and Mrvar 1998) and UCINET (Borgatti, Everett, and Freeman 2002). Though these software packages are still existent, the last ten years have witnessed a significant change in the needs and aspiration of researchers working in the field. The growth of popular online social networks, such as Facebook, Twitter, LinkedIn, Snapchat, and the availability of data from large systems such as the telecommunication system and the inter- net of things (IoT) has ushered in the need to focus on computational and data management issues associated with SNA. During this period, several Python (Van Rossum et al. 2011) and Java based SNA tools, such as NetworkX (Hagberg, Schult, and Swart 2008) and SNAP (Leskovec and Sosič 2016), and R (R Core Team 2020) packages, such as statnet (Hunter, Handcock, Butts, Goodreau, and Morris 2008; Handcock et al. -
Network Visualization with Igraph
Using igraph for Visualisations Dr Jamsheed Shorish The Australian National University [email protected] 15 July 2013 - Beihang University Introduction I igraph is a network analysis and visualisation software package, currently for R and Python. I It can be found at igraph.sourceforge.net. I For our course, we will be using igraph for R . Screenshot from the igraph website, depicting an Erdős - Rényi graph and associated colour-coded components, or clusters. Installing igraph for R I Installation of igraph for R is very simple–the command is: > install.packages( 'igraph ') I You may need to specify a particular directory if e.g. you don’t have privileges to install to the system location for R : > install.packages( 'igraph ', lib='my/ package/location ') I To load the library, use > library( 'igraph ',lib) I or > library( 'igraph ', lib. loc='my/package/ location ') Loading a Dataset I The first thing to do is to get some data! I For consistency I’ll assume that all data is loaded in graphml format. I This can be exported by the Python networkx package. I To load a network dataset from a file, use: > G = read.graph( 'network.graphml ', format ='graphml ') I Confirm that your dataset is recognised by igraph : > G IGRAPHD-W- 560 1257 -- \ + attr: label (v/c), id (v/c), weight (e/n ), id (e/c) Layout of a Graph I To visualise a network well, use the layout function of igraph to specify the layout prior to plotting. I Different networks work best with different layouts–this is more art than science. -
Performing Knowledge Requirements Analysis for Public Organisations in a Virtual Learning Environment: a Social Network Analysis
chnology Te & n S o o ti ft a w a m r r e Fontenele et al., J Inform Tech Softw Eng 2014, 4:2 o f E Journal of n n I g f i o n DOI: 10.4172/2165-7866.1000134 l e a e n r r i n u g o J ISSN: 2165-7866 Information Technology & Software Engineering Research Article Open Access Performing Knowledge Requirements Analysis for Public Organisations in a Virtual Learning Environment: A Social Network Analysis Approach Fontenele MP1*, Sampaio RB2, da Silva AIB2, Fernandes JHC2 and Sun L1 1University of Reading, School of Systems Engineering, Whiteknights, Reading, RG6 6AH, United Kingdom 2Universidade de Brasília, Campus Universitário Darcy Ribeiro, Brasília, DF, 70910-900, Brasil Abstract This paper describes an application of Social Network Analysis methods for identification of knowledge demands in public organisations. Affiliation networks established in a postgraduate programme were analysed. The course was ex- ecuted in a distance education mode and its students worked on public agencies. Relations established among course participants were mediated through a virtual learning environment using Moodle. Data available in Moodle may be ex- tracted using knowledge discovery in databases techniques. Potential degrees of closeness existing among different organisations and among researched subjects were assessed. This suggests how organisations could cooperate for knowledge management and also how to identify their common interests. The study points out that closeness among organisations and research topics may be assessed through affiliation networks. This opens up opportunities for ap- plying knowledge management between organisations and creating communities of practice. -
A Comparative Analysis of Large-Scale Network Visualization Tools
A Comparative Analysis of Large-scale Network Visualization Tools Md Abdul Motaleb Faysal and Shaikh Arifuzzaman Department of Computer Science, University of New Orleans New Orleans, LA 70148, USA. Email: [email protected], [email protected] Abstract—Network (Graph) is a powerful abstraction for scalability for large network analysis. Such study will help a representing underlying relations and structures in large complex person to decide which tool to use for a specific case based systems. Network visualization provides a convenient way to ex- on his need. plore and study such structures and reveal useful insights. There exist several network visualization tools; however, these vary in One such study in [3] compares a couple of tools based on terms of scalability, analytics feature, and user-friendliness. Due scalability. Comparative studies on other visualization metrics to the huge growth of social, biological, and other scientific data, should be conducted to let end users have freedom in choosing the corresponding network data is also large. Visualizing such the specific tool he needs to use. In this paper, we have chosen large network poses another level of difficulty. In this paper, we five widely used visualization tools for an empirical and identify several popular network visualization tools and provide a comparative analysis based on the features and operations comparative analysis. Our comparisons are based on factors these tools support. We demonstrate empirically how those tools such as supported file formats, scalability in visualization scale to large networks. We also provide several case studies of and analysis, interacting capability with the drawn network, visual analytics on large network data and assess performances end user-friendliness (e.g., users with no programming back- of the tools. -
Week 2 - Handling Real-World Network Datasets
28/12/2017 Social Networks - - Unit 6 - Week 2 - Handling Real-world Network Datasets X [email protected] ▼ Courses » Social Networks Unit 6 - Week Announcements Course Forum Progress Mentor 2 - Handling Real-world Network Datasets Course outline Week2-assignment1 Course Trailer . FAQ 1) Citation Network is which type of network? 1 point Things to Note Directed Accessing the Portal Undirected Week 1 - Introduction Accepted Answers: Week 2 - Handling Real-world Network Directed Datasets 2) Co-authorship Network is which type of network? 1 point Directed Undirected Lecture 14 - Introduction to Datasets Accepted Answers: Undirected 3) What is the full form of CSV? 1 point Computer Systematic Version Comma Separated Version Comma Separated Values Computer Systematic Values Lecture 15 - Ingredients Network Accepted Answers: Comma Separated Values 4) Which of the following is True with respect to the function read_weighted_edgelist() 1 point This function can be used to read a weighted network dataset, however, the weights should only be in string format. Lecture 16 - This function can be used to read a weighted network dataset, however, the weights should only be numeric. Synonymy Network This function can be used to read a weighted network dataset and the weights can be in any format. The statement is wrong; such a function does not exist. Accepted Answers: This function can be used to read a weighted network dataset, however, the weights should only be numeric. Lecture 17 - Web 5) Choose the one that is False out of the following: 1 point Graph GML stands for Graph Modeling Language. https://onlinecourses.nptel.ac.in/noc17_cs41/unit?unit=42&assessment=47 1/4 28/12/2017 Social Networks - - Unit 6 - Week 2 - Handling Real-world Network Datasets GML stores the data in the form of tags just like XML. -
Luatex Lunatic
E34 MAPS 39 Luigi Scarso LuaTEX lunatic And Now for Something Completely Different Examples are now hosted at contextgarden [35] while – Monty Python, 1972 [20] remains for historical purposes. Abstract luatex lunatic is an extension of the Lua language of luatex to permit embedding of a Python interpreter. Motivations & goals A Python interpreter hosted in luatex allows macro pro- TEX is synonymous with portability (it’s easy to im- grammers to use all modules from the Python standard li- plement/adapt TEX the program) and stability (TEX the brary, allows importing of third modules, and permits the language changes only to Vx errors). use of existing bindings of shared libraries or the creation of We can summarize by saying that “typesetting in T X new bindings to shared libraries with the Python standard E module ctypes. tends to be everywhere everytime.” Some examples of such bindings, particularly in the area of These characteristics are a bit unusual in today’s scientific graphics, are presented and discussed. scenario of software development: no one is surprised Intentionally the embedding of interpreter is limited to the if programs exist only for one single OS (and even for python-2.6 release and to a luatex release for the Linux op- a discontinued OS, given the virtualization technology) erating system (32 bit). and especially no one is surprised at a new release of a program, which actually means bugs Vxed and new Keywords features implemented (note that the converse is in some Lua, Python, dynamic loading, ffi. sense negative: no release means program discontinued). -
A Walk on Python-Igraph
A Walk on Python-igraph Carlos G. Figuerola - ECyT Institute University of Salamanca - 17/04/2015 Carlos G. Figuerola: A walk on Python-igraph i-graph · i-graph is a library · written in C++ (fast and memory e�cient) · a tool for programmers · it works from programs in R, Python (and C++, btw) here, we are going to work with Python + i-graph A walk on Python-igraph 3/46 Carlos G. Figuerola: A walk on Python-igraph Python · a programming language intended for scripting · interpreted language (semi-compiled/bytecodes, actually) · it is free software (runing on Linux, Win, Mac machines) · it has a lot of modules for no matter about wich task · it is very intuitive but ... it has powerfull data structures, but complex A walk on Python-igraph 4/46 Carlos G. Figuerola: A walk on Python-igraph Installing i-graph · Web Home at http://igraph.org/ · in general you will need Python and a C/C++ compiler. Use of pip or easy_install is more convenient · Linux: it is in main repositories, better install from there · Win: there are (unno�cial) installers in: http://www.lfd.uci.edu/~gohlke /pythonlibs/#python-igraph (see instructions on top of this page) · Mac: see https://pypi.python.org/pypi/python-igraph A walk on Python-igraph 5/46 Carlos G. Figuerola: A walk on Python-igraph i-graph: pros & cons · it can deal with big amounts of data · it has a lot of measures, coe�cients, procedures and functions · it is �exible and powerfull, can be used with ad-hoc programs/scripts or in interactive way (commands from a text terminal) · no (native) graphic interface, no mouse nor windows · not very good branded graphics (although yo can import/export easily from/to another software) A walk on Python-igraph 6/46 Carlos G. -
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… -
Using Computer Games Techniques for Improving Graph Visualization Efficiency
Poster Abstracts at Eurographics/ IEEE-VGTC Symposium on Visualization (2010) Using Computer Games Techniques for Improving Graph Visualization Efficiency Mathieu Bastian1, Sebastien Heymann1;2 and Mathieu Jacomy3 1INIST-CNRS, France 2IMS CNRS - ENSC Bordeaux, France 3WebAtlas, Telecom ParisTech, France Abstract Creating an efficient, interactive and flexible unified graph visualization system is a difficult problem. We present a hardware accelerated OpenGL graph drawing engine, in conjunction with a flexible preview package. While the interactive OpenGL visualization focuses on performance, the preview focuses on aesthetics and simple network map creation. The system is implemented as Gephi, a modular and extensible open-source Java application built on top of the Netbeans Platform, currently in alpha version 0.7. Categories and Subject Descriptors (according to ACM CCS): H.5.2 [Information Systems]: User Interfaces—I.3.6 [Computing Methodologies]: Methodology and Techniques—Interaction techniques 1. Introduction For guaranteeing best performance, the 3-D visualization engine leaves apart effects and complex shapes, for instance Large scale graph drawing remains a technological chal- curved edges and focus on basics, i.e. nodes, edges and la- lenge, actively developed by the infovis community. Many bels. As it is specialized in a particular task, the visual- systems have successfully brought interactive graph visu- ization engine is thus less flexible. That’s why a comple- alization [Aub03], [Ada06], [TAS09] but still have visu- mentary drawing library, named Preview is needed to bring alization limitations to render large graphs at some mo- parametrization and flexibility. The following sections de- ments. Various techniques have been used to either optimize scibe the role granted to each visualization module.