Igraph Tutorial

Orange Box Ceo 6,520,065 views. Following on from last time, this tutorial will focus on more advanced graph techniques and existing algorithms such as Dijkstra's algorithm that can be used to draw real meaning from graphs. Anyone got library or code suggestions on how to actually plot a couple of sample trees from: getTree(rfobj, k, labelVar=TRUE) (Yes I know you're not supposed to do this operationally, RF is a. The Tutorial was presented by Olaf Menzer in a workshop at the ODSC West Conference in San Francisco in 2018. , two samples with identical DNA and IDs) the original data. This document was originally prepared for a Japanese workshop on the software R; however, it has been updated through interaction. Visualization of Duplicates. …That's where igraph comes in. I will provide four examples with different types of data where I take it from its raw form and prepare it for further plotting and analysis using the statnet. If you use Cytoscape from igraph, you can use variety of network analysis functions in igraph and visualize the result with powerful visualization tools available in Cytoscape. You need to do the following steps. Graph theory can help you understand your data beyond the experiment by looking e. Deprecated: Function create_function() is deprecated in /home/forge/primaexpressinc. You will learn how to use the igraph R package to explore and analyze social network data as well as learning how to visualize networks. In this tutorial, we demonstrate how to use Monocle 3 (alpha version) to perform clustering for very large datasets and then identify marker genes specific for each cluster. During this lesson, you will learn what a graph database is, how RDF defines one, and visualise graph data so you can get a feel of what it looks like. This page is a companion for the SIAM Review paper on power-law distributions in empirical data, written by Aaron Clauset (me), Cosma R. SPSS Graphs. igraph provides a huge amount of facilities for those who want to do any analysis on networks, from elementary aspects to advanced ones like shortest path, community detection and clustering, network traffic analysis and so forth. There are many applications of linear algebra; for example, chemists might use row reduction to get a clearer picture of what elements. Department of Mathematics and Statistics WIKI Service - A few tutorials… CRAN: Contributed Documentation - Another long list of tutorials, in different languages. The links to all these modules are can be found in the list of resources. zip: iGrafx Process Automation Usage Scenario: Example use case of iGrafx Process Automation for improving an employment application process [720 Kb]. Among the new major new features and changes in the 3. Other layouts. Network Analysis and visualization appears to be an interesting tool to give the researcher the ability to see its data from a new angle. #this program demonstrates some of the community detection tools #commonly used in R to partition networks. This package facilitates the creation and rendering of graph descriptions in the DOT language of the Graphviz graph drawing software from Python. Introduction. The osmar package smoothly integrates the OpenStreetMap project into the R ecosystem. For examples running the older plot_network function, which may provide some added flexibility with igraph objects, see the plot_network section later. 7 Functions to do Metric Multidimensional Scaling in R Posted on January 23, 2013. Defining centrality indices in the netrankr package is explained here. It provides a brief overview of network formats, focusing on their structure and representation in key R. Fortunately, the C library igraph provides such a collection of network analysis tools and has an interface in R, that is, the igraph package [23] but where the computations have to be carried out with a special class of graphs in R, namely ‘igraph. The video shows how a network grows through the preferential attachment mechanism. Basic graph analytics using igraph Thanks for the tutorial, is a very good start point for network analysis with i-graph May 9, 2013 at 11:53 AM. Further documentation on using igraph can be found here. The Topcoder Community includes more than one million of the world’s top designers, developers, data scientists, and algorithmists. Gephi version 0. 6, Python 2. Practical statistical network analysis (with R and igraph) G´abor Cs´ardi [email protected] zip PyGraphistry: Explore Relationships. Following on from last time, this tutorial will focus on more advanced graph techniques and existing algorithms such as Dijkstra's algorithm that can be used to draw real meaning from graphs. php(143) : runtime-created function(1) : eval()'d code(156) : runtime-created. Network analysis with the igraph package Related Examples. In this video, you learn how to download and ready for use the sna and igraph modules. The data analysis language R provides a rich and varied environment. Download the one that is suitable for your Python version (currently there are binary packages for Python 2. It also includes information on editing the graphs, and printing selected parts of the output. R offers a set of packages called the html widgets: they allow to build interactive dataviz directly from R. Ian Rogers IPR Computing Ltd. It is highly recommended to read it at least once if you are new to igraph. KNN is extremely easy to implement in its most basic form, and yet performs quite complex classification tasks. igraph allows you to generate a graph object and search for communities (clusters or modules) of related nodes / vertices. igraph allows you to generate a graph object and search for communities (clusters or modules) of related nodes / vertices. There are many functions to create different graph structures in Igraph. Part I: Introductory Materials Introduction to Graph Theory Dr. Try this tutorial. This simple "tutorial" is not meant to be a comprehensive introduction to R or even the network package in R. We will mainly introduce 1) use delayedarray to facilitate calculations in functions estimateSizeFactor, estimateDispersions and preprocessCDS, etc for large datasets. 2) Argimiro Arratia & Marta Arias September 18, 2018 1 Introduction This session will introduce the basic tools for the analysis of networks that we will be using during this course. Recent in igraph. Because every feature of SymPy must have a test case, when you are not sure how to use something, just look into the tests/ directories, find that feature and read the tests for it, that will tell you everything you need to know. > simpleNetwork For very basic force directed network graphics you can use simpleNetwork. Okay, thank you. The official home of the Python Programming Language. Checked library location for my R (it is, by the way, C:\Program Files\R\R-3. School of Journalism. The igraph R package allows exporting graphs as edge lists, which can be imported into Cytoscape. frame, with id column; an edges data. A graph with no edges make_empty_graph: A graph with no edges in igraph: Network Analysis and Visualization rdrr. With ggplot2, shapes and line types can be assigned overall (e. If you use cxnet with igraph you will have a class Network. CAVNet is an R package that facilitates Creation Analysis and Visualization of Networks. " Bender-deMoll, Skye and Daniel A. I am not an expert on Igraph, I just transformed a lab workshop into this tutorial hoping it would be useful for other people outside the lab. SparCC is a network inference tool that was specifically designed to be robust to data compositionality. SocialNetworkAnalysis: CentralityMeasures DongleiDu ([email protected] Introduction The main goals of the igraph library is to provide a set of data types. If you are unfamiliar with R, there are many online tutorials; you can nd many using Google, for example. com This post presents an example of social network analysis with R using package igraph. ##### ##### # LAB 1 - Introductory Lab # The point of this lab is to introduce students to the packages of # SNA and Igraph, to cover some basic R commands, to load and manage # data, to generate graph visualizations, and to export the data for # use elsewhere. Using hover, you can still use click to set a view :. It accepts any object that can be coerced to the network class, including adjacency or incidence matrices, edge lists, or one-mode igraph network objects. By Andrie de Vries, Joris Meys. Introdution. Let's load in the Karate network from Network Example Data. More advanced is Eric D. 1 Clique percolation. 50 revealed the higher values of γ were able to reveal the true network structure but that the value of 0 included a. Using the iGraph package to Analyse the Enron Corpus. Estou certo de que isso não só lhe deu uma ideia sobre métodos básicos de análise de dados, mas também mostrou como implementar algumas das técnicas mais sofisticadas disponíveis hoje. In this chapter, you will be introduced to fundamental concepts in social network analysis. edu if you applied to Graduate Studies or [email protected] Scanpy is a scalable toolkit for analyzing single-cell gene expression data. Using R to Detect Communities of Correlated Topics. So far this book has focussed on tibbles and packages that work with them. Download the. igraph can be programmed in R, Python, Mathematica and C/C++. Psychological Methods , doi: 10. I am a little out of the cultural loop, so I don’t have a lot of contextual knowledge, but the hottest network analysis lately is about Game of Thrones, so…. An interactive charts allows the user to perform actions: zooming, hovering a marker to get a tooltip, choosing a variable to display and more. (This is a temporary download meant to fix SoNIA. DictList() and Graph. It gives a good overview of the potential in using R as well as introducing a number of interesting statistical ideas. igraph provides a huge amount of facilities for those who want to do any analysis on networks, from elementary aspects to advanced ones like shortest path, community detection and clustering, network traffic analysis and so forth. I currently have an adjacency matrix, but cannot get the graph. iGrafx BPM—Business Process Management and Analysis solutions assist businesses in achieving continuous process improvement and sustained process excellence. By Siu Kwan Lam | March 10, Then check out the Numba tutorial for CUDA on the ContinuumIO github repository. Another iGraph tutorial. The very basics from a tutorial at the University of Waterloo. R Language • Examples (1). Gephi version 0. The ‘igraph’ package contains the function ‘page. For example, sociologist are eager to understand how people influence the behaviors of their peers; biologists wish to learn how proteins regulate the actions of other proteins. This will add a select box which lets you choose the theme. While pathpy does not depend on any specific graph library, for illustration purposes this tutorial will use network visualizations generated by python-igraph. zip was there. com/public/qlqub/q15. Ce TP-ci va présenter les propriétés locales. Graph, with the Fruchterman-Reingold layout. frame, with from and to columns, which make the link with id. igraph: Network Analysis and Visualization. Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R. The igraph library provides versatile options for descriptive network analysis and visualization in R, Python, and C/C++. …While different methods return different results,…in general, the higher the betweenness score…associated with a vertex, the more of a bridging role…it plays within. igraph is utilised in the R implementation of the popular Phenograph cluster and community detection algorithm (used in scRNA-seq and mass cytometry), and also in the popular scRNA-seq package Seurat. com/public/1zuke5y/q3m. You can also use igraph to build your graph data and then use the igraph_to_networkD3 function to convert this data to a suitable object for networkD3 plotting. Millman, Vincent Rouvreau Abstract This tutorial gives an introduction to the R package TDA, which provides some tools for. The ggnet2 function is a visualization function to plot network objects as ggplot2 objects. Gephi version 0. - [Narrator] In social networks, some individuals…provide more bridges between and among groups…of network members. If you’re doing community detection, make sure to get the louvain-igraph module that adds the most cutting edge algorithms to iGraph. If you’re reading this then you already know how to use pivot tables in Excel or have hopefully already gone through my previous tutorial, Introduction to R for Excel users. Node placement and the Fruchterman-Reingold algorithm. Dash Club is a no-fluff, twice-a-month email with links and notes on the latest Dash developments and community happenings. Although I have used it sparingly, the R package brainGraph has some useful functions that lend themselves to brain graph analysis in particular. Graph-tool performance comparison. As time goes by, my own workflow leans more heavily on R’s igraph package, plus the JavaScript library D3 to create web-based visualizations. Once computed, we will add them to the graph object use the "set. Which igraph is right for you? Installation from a binary package; Compiling igraph from source; Summary; Tutorial. igraph() function: vertex. 08/16/2019; 16 minutes to read +5; In this article. The salient topological features of data can be quanti ed with persistent homology. a nodes data. Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a. You can use the rescale=FALSE argument to plot() and then no rescaling is performed. SoNIA is a Java-based package for visualizing dynamic or longitudinal "network" data. Introduction []. Efficient classes and algorithms for network analysis has been available in R for a long time with e. Using the iGraph package to Analyse the Enron Corpus. CREATING GRAPHS. iGraph read_graph(). You can get it here. The package iGraph offers multiple graph visualizations for graph objects. It uses the ggnet package extensively, and the ggnet2 function. In this first tutorial, we will show you how to create a graph, add three nodes and three edges, remove an edge and a node, and finally, print the result on the standard output. 6-2 Bayesian Networks in the Absence of Temporal Information. Well ggraph have access to all the layouts implemented in igraph, and then some (more on that later). The review of Igraph tutorial was not enough and the search in code communities started. R packages in the Power BI service. Two of the advantages of so many agencies using a standardized data format is that it makes it easier for us (1) to apply the same research methods to different cities and do comparative studies, and (2) to share our scripts, get feedback and learn from others. What are social networks? 50 xp Creating an igraph object 100 xp Counting vertices and edges. Graph clustering is the task of grouping the vertices of the graph into clusters taking into consideration the edge structure of the graph in such a way that there should be many edges within each cluster and relatively few between the clusters. The client software is a graph component with an optional application wrapper that is integrated into an existing web interface. Luke, A User's Guide to Network Analysis in R is a very useful introduction to network analysis with R. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R. I start from scratch and discuss how to construct and customize almost any ggplot. Introduction. For Project Mosaic, I’m researching UNCC publications in social science and computing & informatics by analyzing the abstract text and the co-authorship social network. What is graph-tool?. Tutorial based on workshop at Rutgers University. Apart from basic linear algebra, no particular mathematical background is required from the reader. Try this tutorial. 7alpha2 was used to do this tutorial. 5, groups. igraph can be programmed in R, Python, Mathematica and C/C++. #A short tutorial on analyzing ego network data in R #for the Social Networks and Health #workshop at Duke University on May 17, 2016 #In this tutorial we learn some basic functionality #for R with dealing with ego network # #We will assume that the data were collected in a traditional #manner, with independently sampled respondents. This part of the excercise tests your NetworkX and iGraph installations by exploring a Network Science dataset. It has been mostly used in the context of microbial ecology in companion with QIIME datasets, but the basic functionality applies to any type of count data or any existing igraph networks. 1 CREATING GRAPHS TherearemanyfunctionstocreatedifferentgraphstructuresinIgraph. igraph vs statnet: A comparison of igraph vs statnet, and a tutorial on using both tools on social network analysis; A hands-on tutorial on statnet (For statnet, check out the Resources page which includes this tutorial) Network Analysis and Visualization with R and igraph: A hands-on tutorial on igraph including a brief R basics. Installation from a binary package igraph on Windows There is a Windows installer for igraph's Python interface on thePython Package Index. GraphX graph processing library guide for Spark 2. Use the GraphHttpClient class to make calls to the Microsoft Graph REST API. [email protected] If you are unfamiliar with R, there are many online tutorials; you can nd many using Google, for example. What is igraph? Things you should know before starting out; Reporting bugs and providing feedback; Installing igraph. 50 revealed the higher values of γ were able to reveal the true network structure but that the value of 0 included a. A Short Tutorial on Graph Laplacians, Laplacian Embedding, and Spectral Clustering Radu Horaud INRIA Grenoble Rhone-Alpes, France Radu. New to Plotly? Plotly's R library is free and open source! Get started by downloading the client and reading the primer. If you find the materials useful, please cite them in your work - this helps me make the case that open publishing of digital materials like this […]. iGrafx BPM—Business Process Management and Analysis solutions assist businesses in achieving continuous process improvement and sustained process excellence. If you use Cytoscape from igraph, you can use variety of network analysis functions in igraph and visualize the result with powerful visualization tools available in Cytoscape. With a great igraph tutorial here. Sinceyoucancreateoneoftwotypesofgraph. Say we are interested in the most central node of the graph and simply compute some standard centrality scores with the igraph package. The Complete Python Graph Class In the following Python code, you find the complete Python Class Module with all the discussed methodes: graph2. However, igraph is not needed to use pathpy unless you wan to visualize higher. packages(igraph). iGrafx Inbound Call Center Tutorial: Tutorial for optimizing an inbound call center using iGrafx® modeling and simulation capabilities. “Drug Deal” Network Analysis with Gephi (Tutorial) R and python both have a package called igraph that does this stuff too. Things you should know before starting out¶. All software tools are buggy to some extent. 7 Step 3: Unpack and compile # tar -zxvf python-igraph-. Yet I hope the glimpses provided above will give you the interest and the ability to look deeper. qgraph qgraph BIG 5 DATASET # Load big5 dataset: data(big5) data(big5groups) # Correlations: Q <- qgraph(cor(big5), minimum = 0. igraph is a library and R package for network analysis. Why use R to do SNA? Examples of SNA in R Additional Resources but not igraph Drew Conway Social Network Analysis in R. I have the following R MWE making use of igraph (manual). Comparison between igraph and networkx I have seen the draft for the new tutorial for the igraph python package and it was very helpful. Further documentation on using igraph can be found here. Sinceyoucancreateoneoftwotypesofgraph. All dots will be connected. Graph Optimization with NetworkX in Python With this tutorial, you'll tackle an established problem in graph theory called the Chinese Postman Problem. If it is 'all' then all edges are used (this was the behavior in igraph 0. It is highly recommended to read it at least once if you are new to igraph. shape=## label. Isomorphic Graphs. If it doesn't work, it means that Cairo was not installed properly (and this is what causes igraph to print "plotting not available"). Fasy, Jisu Kim, Fabrizio Lecci, Cl ement Maria, David L. Type "import cairo" at your Python prompt. In this exercise, we are doing a quick first pass on the network data generated from the Republic of Texas correspondence. CREATING GRAPHS. The data to analyze is Twitter text data of @RDataMining used in the example of Text Mining, and it can be downloaded …. by Yanchang Zhao, RDataMining. Use the GraphHttpClient class to make calls to the Microsoft Graph REST API. Practical statistical network analysis (with R and igraph) G´abor Cs´ardi [email protected] Package ‘igraph’ - The Comprehensive R Archive Network. This information is provided for your reference. Tutorial on the R package TDA Jisu Kim Brittany T. Its inputs are the adjacency matrix, to get the dimensions from, and the igraph object corresponding to the matrix. Download the one that is suitable for your Python version (currently there are binary packages for Python 2. Wolfram commence à travailler sur le logiciel en 1986 et en sort la première version en 1988. It will guide you to the basic steps of network visualization and manipulation in Gephi. FlTk Tutorial. You can use the powerful R programming language to create visuals in the Power BI service. Additional software. Igraph Object The description of an igraph object starts with up to four letters: D or U, for a directed or undirected graph. # python setup. The review of Igraph tutorial was not enough and the search in code communities started. It is a great package but I found the documentation somewhat difficult to use, so hopefully this post can be a helpful introduction to network visualization with R. ca) Faculty of Business Administration, University of New Brunswick, NB Canada Fredericton. • igraph_layout_circle – nodes organized in a circle by ID • igraph_layout_bipartite – standard for bipartite graphs • igraph_layout_fruchterman_reingold – FDL, flexible and usually attractive • igraph_layout_kamada_kawai – also FDL, usually faster and messier than FR • igraph_layout_lgl – for large graphs. r-bloggers. 5 series, compared to 3. Boolean includeShapes. While working on new graph functions for my package toaster I had to pick from the R packages that represent graphs. A tutorial with a simple numerical example is posted below with the code. Social Network Analysis: Introduction Donglei Du ([email protected] Gephi file formats. PyGraphistry PyGraphistry is library to extract, transform, and visually explore big graphs Install with pip View on GitHub Download. It will guide you to the basic steps of network visualization and manipulation in Gephi. Created using Sphinx 0. user manual igraph - Ebook download as PDF File (. ) This is the best igraph tutorial I've found. Search: Search Cpickle load example. By Andrie de Vries, Joris Meys. 0_45) JDK/JRE on 32-bit and 64-bit Ubuntu operating systems. Given a igraph object g, one way to get the node names is to write V(g). If you use Cytoscape from igraph, you can use variety of network analysis functions in igraph and visualize the result with powerful visualization tools available in Cytoscape. As an exercise, you can try to import the SPIEC-EASI graph into Cytoscape using igraph's write. table R package is considered as the fastest package for data manipulation. …That's where igraph comes in. The data to analyze is Twitter text data of @RDataMining used in the example of Text Mining, and it can be downloaded …. Estos incluyen degreenet, RSeina, PAFit, igraph, red SNA, tneto, ERGM, Bergm, hergm, latentnet y networksis. Especially with visualization. igraphは Gabor Csardi と Tamas Nepusz によってメンテナンスされています。igraph ライブラリを使うことで、R, Python, C/C++ 言語において様々な方法でネットワークの解析や可視化ができます。このワークショップでは R での igraph を扱います。. igraph allows you to generate a graph object and search for communities (clusters or modules) of related nodes / vertices. Kolaczyk and Gábor Csárdi’s, Statistical Analysis of Network Data with R (2014). The XPI program was developed to quantify parallel reaction monitoring (PRM) data of stable isotope labeled peptides. Social network analysis with R sna package George Zhang iResearch Consulting Group (China) [email protected] Plotly shapefile. Written in a reader-friendly style, it covers the types of graphs, their properties, trees, graph traversability, and the concepts of coverings, coloring, and matching. 7alpha2 was used to do this tutorial. Suppose we would like to examine if our supposely duplicate data are matching (i. , two samples with identical DNA and IDs) the original data. net NetSciX 2016 School of Code Workshop, Wroclaw, Poland Contents. Notice: Undefined index: HTTP_REFERER in /home/forge/shigerukawai. The contents are at a very approachable level throughout. Sebastian Wolf was co-implementing this…. Routines for simple graphs and network analysis. frame, with from and to columns, which make the link with id. frame, with from and to columns, which make the link with id. js, released under the Apache 2. You can get it here. Simple Directed and Non-directed Network Graphing. I installed igraph using the command below: sudo apt-get install python3-pip sudo pip3 install igraph when I run a tool I have below error: raise DeprecationWarning("To avoid name collision with the igraph project, "DeprecationWarning: To avoid name collision with the igraph project, this visualization library has been renamed to 'jgraph'. Using hover, you can still use click to set a view :. It is a great package but I found the documentation somewhat difficult to use, so hopefully this post can be a helpful introduction to network visualization with R. php(143) : runtime-created function(1) : eval()'d code(156) : runtime-created. The client software is a graph component with an optional application wrapper that is integrated into an existing web interface. Notice: Undefined index: HTTP_REFERER in /home/forge/shigerukawai. (This is a temporary download meant to fix SoNIA. Scanpy is a scalable toolkit for analyzing single-cell gene expression data. Estos incluyen degreenet, RSeina, PAFit, igraph, red SNA, tneto, ERGM, Bergm, hergm, latentnet y networksis. Please send comments or suggestions on accessibility to [email protected] qgraph qgraph BIG 5 DATASET # Load big5 dataset: data(big5) data(big5groups) # Correlations: Q <- qgraph(cor(big5), minimum = 0. by Yanchang Zhao, RDataMining. This also implies that any process generating an exact Zipf rank distribution must have a strictly power-law probability density function. Get the tutorial PDF and code, or download on GithHub. igraph – The network analysis package. This page contains the common catches when using the igraph R package. py install If you encounter…. Whatever you use, you just need to. net NetSciX 2016 School of Code Workshop, Wroclaw, Poland Contents. to be connected. 5 release series are. The newer plot_net function does not require a separate make_network function call, or a separate igraph object. Thus the rich gets richer in social connectivity. [email protected] I have the following R MWE making use of igraph (manual). What is a Wiki Site? How to edit pages? How to join this site? Site. New in qgraph are the option to include constraints on the nodes by fixing a coordinate for nodes or reducing the maximum allowed displacement per node. In this chapter, you will be introduced to fundamental concepts in social network analysis. igraph seems to be more efficient than statnet, and the many of the functions (particularly manipulating data and dealing with vertex attributes) seem more intuitive to me. Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a. php(143) : runtime-created function(1) : eval()'d code(156) : runtime-created. I really appreciate your help in advance. If you are an applicant and cannot find your information, please contact [email protected] igraph provides a huge amount of facilities for those who want to do any analysis on networks, from elementary aspects to advanced ones like shortest path, community detection and clustering, network traffic analysis and so forth. Tutorial de igraph (analisis de redes sociales) Análisis de una comunidad de windsurfers Para este tutorial vamos a utilizar los datos de dos comunidades de windsurfers en una playa del sur de California, en 1986. to be connected. The igraph plot for the second clustered family looks like below. The data to analyze is Twitter text data of @RDataMining used in the example of Text Mining, and it can be downloaded …. Download the one that is suitable for your Python version (currently there are binary packages for Python 2. 04 Droplet; Non-root user with sudo privileges (Initial Server Setup with Ubuntu 16. and Daniel A. txt) and real node file (real_node_table. Visualization of Duplicates. Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Making Cytoscape Networks¶. Get some practice dealing with network data with R, the adjacency matrix, igraph, and more for data visualization. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more. You can set up Plotly to work in online or offline mode. Compiling igraph from source tells you how to compile igraph from the source package. The Tutorial was presented by Olaf Menzer in a workshop at the ODSC West Conference in San Francisco in 2018. Boolean includeShapes. , for a local regression application, see [21,22]). Further documentation on using igraph can be found here. The contents are at a very approachable level throughout. This tutorial includes various examples and practice questions to make you familiar with the package. 2 A Brief R Tutorial 2. js library,…which really excels in this area. Because Gephi is an easy access and powerful network analysis tool, we propose a tutorial designed to allow everyone to make his first experiments on two complementary datasets. This tutorial will show you how to use SPSS version 12. Two of the advantages of so many agencies using a standardized data format is that it makes it easier for us (1) to apply the same research methods to different cities and do comparative studies, and (2) to share our scripts, get feedback and learn from others. A solution to use this function for weighted graphs has been taken from the igraph package (Csardi G & Nepusz T, 2006) in which the same function was ported from the SNA package. " Bender-deMoll, Skye and Daniel A. dist=## the distance of the vertex label from the center of the vertex. There exists moreover an interface for Mathematica. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: