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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).. -
Cytoscape: the Network Visualization Tool for Genomespace Workflows
F1000Research 2014, 3:151 Last updated: 21 JUL 2021 SOFTWARE TOOL ARTICLE Cytoscape: the network visualization tool for GenomeSpace workflows [version 2; peer review: 3 approved] Barry Demchak1, Tim Hull1, Michael Reich2, Ted Liefeld2, Michael Smoot3, Trey Ideker1, Jill P. Mesirov2 1Department of Medicine, University of California, San Diego, La Jolla, CA, 92093-0688, USA 2Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA , 02142, USA 3Qualcomm, Inc, San Diego, CA , 92121, USA v2 First published: 01 Jul 2014, 3:151 Open Peer Review https://doi.org/10.12688/f1000research.4492.1 Latest published: 26 Aug 2014, 3:151 https://doi.org/10.12688/f1000research.4492.2 Reviewer Status Invited Reviewers Abstract Modern genomic analysis often requires workflows incorporating 1 2 3 multiple best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms version 2 that create data flows between them. One such tool is Cytoscape 3, a (revision) popular application that enables analysis and visualization of graph- 26 Aug 2014 oriented genomic networks. As Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required version 1 special care in creating a seamless user experience and enabling 01 Jul 2014 report report report appropriate data flows. In this paper, we present the design and operation of the Cytoscape GenomeSpace app, which accomplishes 1. Aris Floratos, Columbia University, New this integration, thereby providing critical analysis and visualization functionality for GenomeSpace users. It has been downloaded over York, NY, USA 850 times since the release of its first version in September, 2013. -
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. -
Cytoscape: the Network Visualization Tool for Genomespace Workflows
UC San Diego UC San Diego Previously Published Works Title Cytoscape: the network visualization tool for GenomeSpace workflows. Permalink https://escholarship.org/uc/item/1qr8t9n8 Authors Demchak, Barry Hull, Tim Reich, Michael et al. Publication Date 2014 DOI 10.12688/f1000research.4492.2 Peer reviewed eScholarship.org Powered by the California Digital Library University of California F1000Research 2014, 3:151 Last updated: 15 SEP 2014 SOFTWARE TOOL Cytoscape: the network visualization tool for GenomeSpace workflows [v2; ref status: indexed, http://f1000r.es/47f] Barry Demchak1, Tim Hull1, Michael Reich2, Ted Liefeld2, Michael Smoot3, 1 2 Trey Ideker , Jill P. Mesirov 1Department of Medicine, University of California, San Diego, La Jolla, 92093-0688, USA 2Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, 02142, USA 3Qualcomm, Inc, San Diego, 92121, USA v2 First published: 01 Jul 2014, 3:151 (doi: 10.12688/f1000research.4492.1) Open Peer Review Latest published: 26 Aug 2014, 3:151 (doi: 10.12688/f1000research.4492.2) Invited Referee Responses Abstract Modern genomic analysis often requires workflows incorporating multiple 1 2 3 best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms that create data flows version 1 between them. One such tool is Cytoscape 3, a popular application that published report report report 01 Jul 2014 enables analysis and visualization of graph-oriented genomic networks. As 1 1 1 Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required special care in creating a seamless user experience and enabling appropriate data flows. -
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. -
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). -
Modeling Genomic Regulatory Networks with Big Data
TIGS-1108; No. of Pages 10 Review Modeling genomic regulatory networks with big data Hamid Bolouri Division of Human Biology, Fred Hutchinson Cancer Research Center (FHCRC), 1100 Fairview Avenue North, PO Box 19024, Seattle, WA 98109, USA High-throughput sequencing, large-scale data generation throughput sequencing-based technologies and to compu- projects, and web-based cloud computing are changing tational modeling and analysis. At the same time, GRNs how computational biology is performed, who performs are both complex (i.e., can exhibit hard-to-predict/nonline- it, and what biological insights it can deliver. I review here ar behaviors) and complicated (i.e., they are composed of the latest developments in available data, methods, and large numbers of component parts and interactions). For software, focusing on the modeling and analysis of the this reason, mathematical and computational approaches gene regulatory interactions in cells. Three key findings are essential in GRN research. are: (i) although sophisticated computational resources Cellular behaviors have traditionally been character- are increasingly available to bench biologists, tailored ized as being mediated through highly distinct processes ongoing education is necessary to avoid the erroneous (e.g., DNA replication) and pathways (e.g., the canonical use of these resources. (ii) Current models of the regula- WNT signaling pathway). However, because of widespread tion of gene expression are far too simplistic and need interactions among cellular processes and pathways, the updating. (iii) Integrative computational analysis of large- use of unbiased, genome-wide technologies is essential to scale datasets is becoming a fundamental component of the discovery and characterization of GRNs. -
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. -
Integrative Genomic Analysis by Interoperation of Bioinformatics Tools in Genomespace
Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Qu, Kun et al. “Integrative Genomic Analysis by Interoperation of Bioinformatics Tools in GenomeSpace.” Nature Methods 13.3 (2016): 245–247. As Published http://dx.doi.org/10.1038/nmeth.3732 Publisher Nature Publishing Group Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/105733 Terms of Use Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nat Methods Manuscript Author . Author manuscript; Manuscript Author available in PMC 2016 July 18. Published in final edited form as: Nat Methods. 2016 March ; 13(3): 245–247. doi:10.1038/nmeth.3732. Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace Kun Qu#1, Sara Garamszegi#2, Felix Wu#2, Helga Thorvaldsdottir2, Ted Liefeld2,3, Marco Ocana2,3, Diego Borges-Rivera4, Nathalie Pochet2,5, James T. Robinson2,3, Barry Demchak3, Tim Hull3, Gil Ben-Artzi6,7, Daniel Blankenberg8, Galt P. Barber9, Brian T. Lee9, Robert M. Kuhn9, Anton Nekrutenko8, Eran Segal6, Trey Ideker3, Michael Reich2,3, Aviv Regev2,4,10, Howard Y. Chang1,11, and Jill P. Mesirov2,3 1Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA 2The -
Constructing 5-Chromatic Unit Distance Graphs Embedded in the Euclidean Plane and Two-Dimensional Spheres
Constructing 5-chromatic unit distance graphs embedded in the Euclidean plane and two-dimensional spheres V.A. Voronov,∗ A.M. Neopryatnaya,† E.A. Dergachev‡ [email protected] June 24, 2021 Abstract This paper is devoted to the development of algorithms for finding unit distance graphs with chromatic number greater than 4, embedded in a two-dimensional sphere or plane. Such graphs provide a lower bound for the Nelson–Hadwiger problem on the chromatic number of the plane and its generalizations to the case of the sphere. A series of 5-chromatic unit distance graphs on 64513 vertices embedded into the plane is constructed. Unlike previously known examples, these graphs do not contain the Moser spindle as a subgraph. The construction of 5-chromatic graphs embedded in a sphere at two values of the radius is given. Namely, the 5-chromatic unit distance graph on 372 vertices embedded into the circumsphere of an icosahedron with a unit edge length, and the 5-chromatic graph on 972 vertices embedded into the circumsphere of a great icosahedron are constructed. 1 Introduction This paper is devoted to one of the questions related to the Nelson–Hadwiger problem on the chromatic number of the plane [15, 36], namely the development of algorithms that allow constructing new examples of 5-chromatic unit distance graphs embedded in a plane or a sphere, based on a given 4-chromatic unit distance subgraph. The chromatic number of a subset of the Euclidean space X ⊆ Rn is the minimum number of colors needed to color X, so that the points u, v at a unit distance apart are arXiv:2106.11824v2 [math.CO] 23 Jun 2021 colored differently, i.e.