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Social Network Analysis with R | Examples
 
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Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 19709 Bharatendra Rai
The Basics of Social Network Analysis: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ So you want to get started with social network analysis but need a foundation or a refresher? This video covers exactly what we mean by a “network” and is the start of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
R Lab.1 - Let's Draw a Social Network Graph: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ Let’s try turning some data into a graph for ourselves in R, an open-source statistical program This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Social Network Analysis Using R Programming Language / Analyzing Social Networks /Learn R
 
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This video shows how to use SNA package to analyze social networks in R programming language. Learn the basics of R language and try data science! Ram Subramaniam Stanford
Views: 79529 Ram Subramaniam
Clustering in Social Network Analysis: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ What is clustering or degree distribution, and how do they affect our interpretation of what’s going on in a network? We define these terms in this video. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Importing Social Network Data into R through CSV Files
 
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This video walks through the process of loading social network data into R for use with the package igraph by 1) typing in a short edge list into an R script), 2) importing a CSV file of an edge list, 3) importing a CSV file of an adjacency matrix. Shot for the University of Maine at Augusta
Views: 12782 James Cook
Closeness Centrality & Betweenness Centrality: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ So what then is “closeness” or “betweenness” in a network? How do we figure these things out and how do we interpret them? This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Gephi Tutorial - How to use Gephi for Network Analysis
 
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Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com Gephi is an open-source network analysis software package written in Java that allows us to visualize all kinds of graphs and networks. In this Gephi tutorial, we walk through how Network Analysis can be used to visually represent large data sets in a way that enables the viewer to get a lot of value from the data just by looking briefly at the graph. Watch this video to learn: - What Network Analysis involves - How to use Gephi to visually represent and analyze data sets - Different examples using Gephi
Views: 18887 Fullstack Academy
What is Social Network Analysis?
 
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You use social networks every day, but how can we understand how they work to affect our decisions, our careers, our health, and our histories? The field of Social Network Analysis is the dynamic and highly adaptable group of techniques that let us quantify and understand the complex structures and flows of relationships, thoughts, and things between people around the world. Look at your own social networks at these links: Check your own personal Facebook social network with Touchgraph: http://www.touchgraph.com/facebook Check your own personal LinkedIn social network with Socilab: http://socilab.com/ Check your own personal Twitter social network with Mentionmapp: http://mentionmapp.com/ Social Network Analysis can enrich the research of faculty and the studies of students—look for workshops run by the Duke Network Analysis Center and classes featuring graph theory, network theory, and social networks. Networks are everywhere—what will you discover with them?
Social Media Analytics - Twitter Analysis in R (Example @realDonaldTrump)
 
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Case Study: Donald Trump Twitter (@realDonaldTrump) Analysis Click here to see how to link to Twitter database: https://www.youtube.com/watch?v=ebutXE4MJ3Y (UPDATED) Twitter Analytics in R codes Powerpoint can be downloaded at https://drive.google.com/open?id=0Bz9Gf6y-6XtTNDE5a2V0dXBjWVU How to process tweets with emojis in R? What if there is a gsub utf-8 invalid error? (Example Solution) 1. Use gsub to replace the emojis (utf-8 coding) codes. 2. See slide 7 in the Powerpoint file above.
Views: 6225 The Data Science Show
Network Layouts and Data Structures: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ How do you choose between network layouts and data structures? This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
R Lab.7 - Eigenvector Centrality: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ When we look at our example networks in this Lab, who are the people who are most connected to other highly connected people, and what surprises are hidden in the data? This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Social Network Analysis With R
 
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Social Network Analysis With R
Views: 137 Chuc Nguyen Van
Dynamic Network Modeling Using R
 
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Step by Step Tutorial for Dynamic Network Modeling using Epimodel, which is an R Package for Mathematical Modeling of Infectious Diseases over Network.
Views: 7270 Stat Pharm
What is Social Network Analysis? by Prof Martin Everett
 
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The focus of social network analysis is on the network of relations. A social network consists of a set of actors (also called nodes or vertices) together with a set of edges (also called arcs) that link pairs of actors. Since edges can share actors (e.g., the A.B edge shares an actor with the B.C edge) this creates a connected web that we think of as a network. For more methods resources see: http://www.methods.manchester.ac.uk
Views: 31962 methodsMcr
Introduction to Positional Features: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ Once we have some basics down, we can start looking at the positional details to learn about the dynamics of a network, like path length and transitivity? We explain these ideas and how they change our analysis of a network. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Introduction to Exploring Social Network Structure with Visualization in R
 
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This video is meant as an introductory excursion into using the research software R to explore social network structure through visualization. Examples of visualization by layout, by node arrangement, and by changing visual node characteristics are provided. Recorded for the University of Maine at Augusta.
Views: 8978 James Cook
In-Degree Centrality: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ You hear a lot about “degree” in network analysis, so what do people mean by in-degree centrality or out-degree centrality? What’s an example of this look like? This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
EARL 2015 - Social Network Analysis in R - Amar Dhand
 
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Dr. Amar Dhand, from Washington University in St. Louis on Social network analysis in R applied to stroke patients' health behaviours at EARL 2015 London - Effective Applications of the R Language For more information see: http://earlconf.com Or, on twitter, follow: http://twitter.com/earlconf
Views: 3124 Mango Solutions
Social Network Analysis
 
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Brief info on how to conduct Social Network Analysis (SNA)
Views: 5697 KMPlus Consulting
Social Network Analysis using R - library
 
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http://r-for-beginners.strategic-leadership-llc-india.com/social-network-analysis/social-network-analysis---2-package-sand library("sand", lib.loc="~/R/win-library/3.1") library("rgl", lib.loc="~/R/win-library/3.1") library("igraph", lib.loc="~/R/win-library/3.1")
Views: 8990 Rohit Dhankar
Introduction: R and IGraph for Edge Lists and Social Network Graphs
 
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This video is a very basic introduction to the use of R in conjunction with the package igraph to take a social network, describe it in the form of an edge list, and generate an image of a network graph. This is intended to be a beginner's video for those entering into the use of R and igraph for the first time, not an encyclopedic reference. Produced for the social science program at the University of Maine at Augusta.
Views: 14478 James Cook
R Lab.6 - Betweenness Centrality: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ So who are the key people in this network--who are the "brokers" who connect the most people together? We take a look at our networks in this lab exercise. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
R Lab.3- Density, Average Path Distance, Degree Distribution:A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ Let's go back to our coding example and take a look at the major structural features of the Discussion and Colleague Networks: Density, Average Path Distance, and Degree Distribution. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Surveys and Stories in Social Networks: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ In this final video of the series, we take a look at critical role that our data sources play in the kinds of analysis we can do. What we ask in a survey can directly affect which analytical method we use, so there's a lot to think about. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Basics of Social Network Analysis
 
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Basics of Social Network Analysis In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research. The freeware sound tune 'MFF - Intro - 160bpm' by Kenny Phoenix http://www.last.fm/music/Kenny+Phoenix was downloaded from Flash Kit http://www.flashkit.com/loops/Techno-Dance/Techno/MFF_-_In-Kenny_Ph-10412/index.php The video's content includes: Why Social Network Analysis (SNA)? Enables us to segment data based on user behavior. Understand natural groups that have formed: a. topics b. personal characteristics Understand who are the important people in these groups. Analysing Social Networks: Data Collection Methods: a. Surveys b. Interviews c. Observations Analysis: a. Computational analysis of matrices Relationships: A. is connected to B. SNA Introduction: [from] A. Directed Graph [to] B. e.g. Twitter replies and mentions A. Undirected Graph B. e.g. family relationships What is Social Network Analysis? Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)). Social Network Analysis Basics: Node and Edge Node: “actor” or people on which relationships act Edge: relationship connecting nodes; can be directional Social Network Analysis Basics: Cohesive Sub-group Cohesive Sub-group: a. well-connected group, clique, or cluster, e.g. A, B, D, and E Social Network Analysis Basics: Key Metrics Centrality (group or individual measure): a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only). b. Measure at the individual node or group level. Cohesion (group measure): a. Ease with which a network can connect. b. Aggregate measure of shortest path between each node pair at network level reflects average distance. Density (group measure): a. Robustness of the network. b. Number of connections that exist in the group out of 100% possible. Betweenness (individual measure): a. Shortest paths between each node pair that a node is on. b. Measure at the individual node level. Social Network Analysis Basics: Node Roles: Node Roles: Peripheral – below average centrality, e.g. C. Central connector – above average centrality, e.g. D. Broker – above average betweenness, e.g. E. References and Reading Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb. American Behavioral Scientist, 43(3), pp. 475-492. Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. In Collaboration Technologies and Systems (CTS), 2014 International Conference on IEEE, pp. 3-8. Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Internet Project.
Views: 38196 Alexandra Ott
Eigenvector Centrality: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ The term “eigenvector centrality” sounds very scientific, but it really means a way to measure how connected someone is to other very connected people. What does an example of this look like, and why might we care? This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Example of basic Social Network Analysis of Facebook friends
 
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http://paddytherabbit.com/example-facebook-friends-analysis/ I am using the Louvain method method for community detection
Views: 5948 David Sherlock
Network Analysis Tutorial: Introduction to Networks
 
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This is the first video of chapter 1 of Network Analysis by Eric Ma. Take Eric's course: https://www.datacamp.com/courses/network-analysis-in-python-part-1 From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere, and knowing how to analyze this type of data will open up a new world of possibilities for you as a Data Scientist. This course will equip you with the skills to analyze, visualize, and make sense of networks. You'll apply the concepts you learn to real-world network data using the powerful NetworkX library. With the knowledge gained in this course, you'll develop your network thinking skills and be able to start looking at your data with a fresh perspective! Transcript: Hi! My name is Eric, and I am a Data Scientist working at the intersection of biological network science and infectious disease, and I'm thrilled to share with you my knowledge on how to do network analytics. I hope we'll have a fun time together! Let me first ask you a question: what are some examples of networks? Well, one example might be a social network! In a social network, we are modelling the relationships between people. Here’s another one - transportation networks. In a transportation network, we are modelling the connectivity between locations, as determined by roads or flight paths connecting them. At its core, networks are a useful tool for modelling relationships between entities. By modelling your data as a network, you can end up gaining insight into what entities (or nodes) are important, such as broadcasters or influencers in a social network. Additionally, you can start to think about optimizing transportation between cities. Finally, you can leverage the network structure to find communities in the network. Let’s go a bit more technical. Networks are described by two sets of items: nodes and edges. Together, these form a “network”, otherwise known in mathematical terms as a “graph”. Nodes and edges can have metadata associated with them. For example, let’s say there are two friends, Hugo and myself, who met on the 21st of May, 2016. In this case, the nodes may be “Hugo” and myself, with metadata stored in a key-value pair as “id” and “age”. The friendship is represented as a line between the two nodes, and may have metadata such as “date”, which represents the date on which we first met. In the Python world, there is a library called NetworkX that allows us to manipulate, analyze and model graph data. Let’s see how we can use the NetworkX API to analyze graph data in memory. NetworkX is typically imported as nx. Using nx.Graph(), we can initialize an empty graph to which we can add nodes and edges. I can add in the integers 1, 2, and 3 as nodes, using the add_nodes_from() method, passing in the list [1, 2, 3] as an argument. The Graph object G has a .nodes() method that allows us to see what nodes are present in the graph, and returns a list of nodes. If we add an edge between the nodes 1 and 2, we can then use the G.edges() method to return a list of tuples which represent the edges, in which each tuple shows the nodes that are present on that edge. Metadata can be stored on the graph as well. For example, I can add to the node ‘1’ a ‘label’ key with the value ‘blue’, just as I would assign a value to the key of a dictionary. I can then retrieve the node list with the metadata attached using G.nodes(), passing in the data=True argument. What this returns is a list of 2-tuples, in which the first element of each tuple is the node, and the second element is a dictionary in which the key-value pairs correspond to my metadata. NetworkX also provides basic drawing functionality, using the nx.draw() function. nx.draw() takes in a graph G as an argument. In the IPython shell, you will also have to call the plt.show() function in order to display the graph to screen. With this graph, the nx.draw() function will draw to screen what we call a node-link diagram rendering of the graph. The first set of exercises we’ll be doing here is essentially exploratory data analysis on graphs. Alright, let’s go on and take a look at the exercises! https://www.datacamp.com/courses/network-analysis-in-python-part-1
Views: 24391 DataCamp
R Lab.2 - Improving the Design and Readability: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ In the last video we started to graph some data, but it needs a lot of improvement. Here we try different options to make the graph more informative and easier to understand. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Network Analysis Tutorial: Network Visualization
 
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This is the 3rd video of chapter 1 of Network Analysis by Eric Ma. Take Eric's course: https://www.datacamp.com/courses/network-analysis-in-python-part-1 From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere, and knowing how to analyze this type of data will open up a new world of possibilities for you as a Data Scientist. This course will equip you with the skills to analyze, visualize, and make sense of networks. You'll apply the concepts you learn to real-world network data using the powerful NetworkX library. With the knowledge gained in this course, you'll develop your network thinking skills and be able to start looking at your data with a fresh perspective! Transcript: You may have seen node-link diagrams involving more than a hundred thousand nodes. They purport to show a visual representation of the network, but in reality just show a hairball. In this section, we are going to look at alternate ways of visualizing network data that are much more rational. I’m going to introduce to you three different types of network visualizations. The first is visualizing a network using a Matrix Plot. The second is what we call an “Arc Plot”, and the third is called “Circos Plot”. Let’s start first with a Matrix Plot. In a Matrix Plot, nodes are the rows and columns of a matrix, and cells are filled in according to whether an edge exists between the pairs of nodes. On these slides, the left matrix is the matrix plot of the graph on the right. In an undirected graph, the matrix is symmetrical around the diagonal, which I’ve highlighted in grey. I’ve also highlighted one edge in the toy graph, edge (A, B), which is equivalent to the edge (B, A). Likewise for edge (A, C), it is equivalent to the edge (C, A), because there’s no directionality associated with it. If the graph were a directed graph, then the matrix representation is not necessarily going to be symmetrical. In this example, we have a bidirectional edge between A and C, but only an edge from A to B and not B to A. Thus, we will have (A, B) filled in, but not (B, A). If the nodes are ordered along the rows and columns such that neighbours are listed close to one another, then a matrix plot can be used to visualize clusters, or communities, of nodes. Let’s now move on to Arc Plots. An Arc Plot is a transformation of the node-link diagram layout, in which nodes are ordered along one axis of the plot, and edges are drawn using circular arcs from one node to another. If the nodes are ordered according to some some sortable rule, e.g. age in a social network of users, or otherwise grouped together, e.g. by geographic location in map for a transportation network, then it will be possible to visualize the relationship between connectivity and the sorted (or grouped) property. Arc Plots are a good starting point for visualizing a network, as it forms the basis of the later plots that we’ll take a look at. Let’s now move on to Circos Plots. A CircosPlot is a transformation of the ArcPlot, such that the two ends of the ArcPlot are joined together into a circle. Circos Plots were originally designed for use in genomics, and you can think of them as an aesthetic and compact alternative to Arc Plots. You will be using a plotting utility that I developed called nxviz. Here’s how to use it. Suppose we had a Graph G in which we added nodes and edges. To visualize it using nxviz, we first need to import nxviz as nv, and import matplotlib to make sure that we can show the plot later. Next, we instantiate a new nv.ArcPlot() object, and pass in a graph G. We can also order nodes by the values keyed on some “key”. Finally, we can call the draw() function, and as always, we also call plt.show(). The code example here shows you how to create an Arc Plot using nxviz, and you’ll get a chance to play around with the other plots in the exercises. Alright! Let’s get hacking! https://www.datacamp.com/courses/network-analysis-in-python-part-1
Views: 8404 DataCamp
Working With Two Mode Social Network Data in R
 
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This video demonstrates two methods for importing and transforming 2-mode network data (also known as bipartite networks or affiliation matrices) in the open-source research program R. Method #1 involves direct input of an affiliation matrix into R, a method good for relatively small matrices. For larger matrices, it's easier to enter affiliation data into a spreadsheet and import a .csv (comma-delimited) file into R. One hypothetical affiliation matrix and one actual affiliation matrix of corporate board interlocks are used to illustrate the development of R scripts for 2-mode network research. This video was created for the University of Maine at Augusta undergraduate social science program.
Views: 5009 James Cook
R Lab.4 - Clustering, In-Degree, & Out-Degree: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ Let's go back to our coding example and get started on the positional features of the Discussion and Colleague Networks by looking at transitivity, in-degree, and out-degree measures. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Social network analysis - Introduction to structural thinking: Dr Bernie Hogan, University of Oxford
 
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Social networks are a means to understand social structures. This has become increasingly relevant with the shift towards mediated interaction. Now we can observe and often analyse links at a scale that far outpaces what was possible only decades ago. While this prompts new methodologies, the large-scale networks we can observe can still be informed by classis questions in social network analysis. In this class, we take a brisk tour through the classic ideas of social network analysis including preferential attachment, small worlds, homophily, the friendship paradox and clustering. Bernie demonstrates how these ideas are not only applicable to modern digital networks but have been updated with interesting insights fromdata on Twitter, Facebook and the World Wide Web itself. This is an introductory class, an advanced class session is planned for 2018. Readings: Hidalgo, C.A. (2016). Disconnected, fragmented, or united? A trans-disciplinary review of network science. Applied Network Science, 1(6), 1-19 . http://doi.org/10.1007/s41109-016-0010-3 Hogan, B. (2017). Online Social Networks: Concepts for Data Collection and Analysis. In Fielding, N.G., Lee, R., & Blank, G. (eds). The Sage Handbook of Online Research Methods. Thousand Oaks, Ca: Sage Publications. Pp. 241-258 Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3047869 Harrington, H.A., Beguerisse-diaz, M., Rombach, M.P., Keating, L. M., & Porter, M.A. (2013). Commentary: Teach network science to teenagers. Network Science, 1(2), 226-247. http://doi.org/10.1017/nws.2013.11 #datascienceclasses
Social Network Analysis
 
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An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 4600 Microsoft Research
Social Network Analysis Overview
 
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See the full course: https://systemsacademy.io/courses/complexity-theory/ Twitter: http://bit.ly/2HobMld A brief overview to the new area of social network analysis that applies network theory to the analysis of social relations. Transcription: Social network analysis is the application of network theory to the modeling and analysis of social systems. it combine both tools for analyzing social relations and theory for explaining the structures that emerge from the social interactions. Of course the idea of studying societies as networks is not a new one but with the rise in computation and the emergence of a mass of new data sources, social network analysis is beginning to be applied to all type and scales of social systems from, international politics to local communities and everything in between. Traditionally when studying societies we think of them as composed of various types of individuals and organizations, we then proceed to analysis the properties to these social entities such as their age, occupation or population, and them ascribe quantitative value to them. This allows social science to use the formal mathematical language of statistical analyst to compare the values of these properties and create categories such as low in come house holds or generation x, we then search for quasi cause and effect relations that govern these values. This component-based analysis is a powerful method for describing social systems. Unfortunately though is fails to capture the most important feature of social reality that is the relations between individuals, statistical analysis present a picture of individuals and groups isolates from the nexus of social relations that given them context. Thus we can only get so far by studying the individual because when individuals interact and organize, the results can be greater than the simple sum of its parts, it is the relations between individuals that create the emergent property of social institutions and thus to understand these institutions we need to understand the networks of social relations that constitute them. Ever since the emergence of human beans we have been building social networks, we live our lives embed in networks of relations, the shape of these structures and where we lie in them all effect our identity and perception of the world. A social network is a system made up of a set of social actors such as individuals or organizations and a set of ties between these actors that might be relations of friendship, work colleagues or family. Social network science then analyze empirical data and develops theories to explaining the patterns observed in these networks In so doing we can begin to ask questions about the degree of connectivity within a network, its over all structure, how fast something will diffuse and propagate through it or the Influence of a given node within the network. lets take some examples of this Social network analysis has been used to study the structure of influence within corporations, where traditionally we see organization of this kind as hierarchies, by modeling the actual flow of information and communication as a network we get a very different picture, where seemingly irrelevant employees within the hierarchy can in fact have significant influence within the network. Researcher also study innovation as a process of diffusion of new ideas across networks, where the oval structure to the network, its degree of connectivity, centralization or decentralization are a defining feature in the way that innovation spreads or fails to spread. Network dynamics, that is how networks evolve overtime is another important area of research, for example within Law enforcement agencies social network analysis is used to study the change in structure of terrorists groups to identify changing relations through which they are created, strengthened and dissolved? Social network analysis has also been used to study patterns of segregation and clustering within international politics and culture, by mapping out the beliefs and values of countries and cultures as networks we can identify where opinions and beliefs overlap or conflict. Social network analysis is a powerful new method we now have that allows us to convert often large and dense data sets into engaging visualization, that can quickly and effectively communicate the underlining dynamics within the system. By combine new discoveries in the mathematics of network theory, with new data sources and our sociological understanding, social network analysis is offering huge potential for a deeper, richer and more accurate understanding, of the complex social systems that make up our world. Twitter: http://bit.ly/2TTjlDH Facebook: http://bit.ly/2TXgrOo LinkedIn: http://bit.ly/2TPqogN
Views: 41281 Systems Academy
Mini Lecture: Social Network Analysis for Fraud Detection
 
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In this mini lecture, Véronique Van Vlasselaer talks about how social networks can be leveraged to uncover fraud. Véronique is working in the DataMiningApps group led by Prof. dr. Bart Baesens at the KU Leuven (University of Leuven), Belgium.
Views: 14761 Bart Baesens
Network Structure
 
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An introduction to social network analysis and network structure measures, like density and centrality. Table of Contents: 00:00 - Network Structure 00:12 - Degree Distribution 02:42 - Degree Distribution 06:17 - Density 10:31 - Clustering Coefficient 11:24 - Which Node is Most Important? 12:10 - Which Node is Most Important? 13:27 - Closeness Centrality 15:01 - Closeness Centrality 16:17 - Closeness Centrality 16:36 - Degree Centrality 17:33 - Betweenness Centrality 17:53 - Betweenness Centrality 20:55 - Eigenvector Centrality 23:02 - Connectivity and Cohesion 24:24 - Small Worlds 26:28 - Random Graphs and Small Worlds
Views: 63048 jengolbeck
Two Mode Networks and One Mode Networks
 
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This video defines, discusses, and works through an example of the difference between 2-mode and 1-mode matrices in social network analysis... and how to move from 2 modes to 1. Shot for the University of Maine at Augusta undergraduate social networks course.
Views: 2523 James Cook
Webinar: An Introduction to Social Network Analysis in Psychology
 
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In this webinar, Dr. Joanna Weill (then a Ph.D. Candidate) provides an overview of Social Network Analysis, a methodology for collecting and analyzing data, and a way of learning about the world that focuses on the relationships between people. This webinar explains what social networks are and what types of psychological questions can be addressed with this methodology. The presenter also discusses strategies for collecting social network data, basic types of data analyses, and useful resources. Please note that although social network analysis can be used to look at online social networks like Facebook and Twitter, this is not the focus of the webinar. This webinar was sponsored by the Society for the Psychological Study of Social Issues (SPSSI) Graduate Student Committee and the American Psychological Association of Graduate Students (APAGS).
Views: 273 SPSSI
Network Analysis. Lecture 18. Link prediction.
 
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Link prediction problem. Proximity measures. Scoring algorithms. Prediction by supervised learning. Performance evaluation. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture18.pdf
Views: 5198 Leonid Zhukov
Enabling qualitative social network analysis
 
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Social network analysis is often thought of as quantitative having its roots in mathematics graph theory. However, that is overlooking the tradition of many qualitative researchers who focus on looking at relationships and interactions among people and within communities. In fact, anthropologists at the University of Manchester (UK) in the 1960s played a major role in the development of qualitative social network analysis. However, the tools that have developed to manage such data have mainly focussed on visualizing networks and/or providing statistical analysis. NVivo 11 Plus’s sociogram tool enables both the visualization of networks as well as drilling easily into the qualitative context. This webinar looks at: • What is qualitative social network analysis? • Qualitative and mixed methods techniques for collecting social network data • How NVivo 11 Plus supports the analysis of qualitative and mixed methods approaches to social network analysis http://www.qsrinternational.com/product/NVivo11-for-Windows/Plus http://www.qsrinternational.com/product/NVivo11-for-Windows/Visualizations#New-Sociograms
Views: 1743 NVivo by QSR
Network Analysis. Lecture10. Community detection
 
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Community detection algorithms. Overlapping communities. Clique percolation method. Heuristic methods. Label propagation. Fast community unfolding. Random walk based methods. Walktrap. Nibble. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture10.pdf
Views: 10753 Leonid Zhukov
Network Centrality
 
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See the full course: https://systemsacademy.io/courses/network-theory/ Twitter: http://bit.ly/2HobMld In this module, we talk about one of the key concepts in network theory, centrality. Centrality gives us some idea of the node's position within the overall network and it is also a measure that tells us how influential or significant a node is within a network although this concept of significance will have different meanings depending on the context. Transcription: In the previous module we talked about the degree of connectivity of a given node in a network and this leads us to the broader concept of centrality. Centrality is really a measure that tells us how influential or significant a node is within the overall network, this concept of significance will have different meanings depending on the type of network we are analyzing, so in some ways centrality indices are answers to the question "What characterizes an important node?" From this measurement of centrality we can get some idea of the nodes position within the overall network. The degree of a node’s connectivity that we previously looked at is probably the simples and most basic measure of centrality. We can measure the degree of a node by looking at the number of other nodes it is connected to vs. the total it could possibly be connected to. But this measurement of degree only really captures what is happening locally around that node it don’t really tell us where the node lies in the network, which is needed to get a proper understanding of its degree centrality and influence. This concept of centrality is quite a bit more complex than that of degree and may often depend on the context, but we will present some of the most important parameters for trying to capture the significance of any given node within a network. The significance of a node can be thought of in two ways, firstly how much of the networks recourses flow through the node and secondly how critical is the node to that flow, as in can it be replaced, so a bridge within a nations transpiration network may be very significant because it carries a very large percentage of the traffic or because it is the only bridge between two important locations. So this helps us understand significance on a conceptual level but we now need to define some concrete parameters to capture and quantify this intuition. We will present four of the most significant metric for doing this here; Firstly as we have already discussed a nodes degree of connectivity is a primary metric that defined its degree of significance within its local environment. Secondly, we have what are called closeness centrality measures that try to capture how close a node is to any other node in the network that is how quickly or easily can the node reach other nodes. Betweenness is a third metric we might use, which is trying to capture the nodes role as a connector or bridge between other groups of nodes. Lastly we have prestige measures that are trying to describe how significant you are based upon how significant the nodes you are connect to are. Again which one of these works best will be context dependent. So to talk about closeness then; closeness maybe defined as the reciprocal of farness where the farness of a given node is defined as the sum of its distances to all other nodes. Thus, the more central a node is the lower its total distance to all other nodes. Closeness can be regarded as a measure of how long it will take to spread something such as information from the node of interest to all other nodes sequentially; we can understand how this correlates to the node’s significance in that it is a measurement of the nodes capacity to effect all the other elements in the network. Twitter: http://bit.ly/2TTjlDH Facebook: http://bit.ly/2TXgrOo LinkedIn: http://bit.ly/2TPqogN
Views: 28554 Systems Academy
Social Networks for Fraud Analytics
 
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Data mining algorithms are focused on finding frequently occurring patterns in historical data. These techniques are useful in many domains, but for fraud detection it is exactly the opposite. Rather than being a pattern repeatedly popping up in a data set, fraud is an uncommon, well-considered, imperceptibly concealed, time-evolving and often carefully organized crime which appears in many types and forms. As traditional techniques often fail to identify fraudulent behavior, social network analysis offers new insights in the propagation of fraud through a network. Indeed, fraud is not something an individual would commit by himself, but is often organized by groups of people loosely connected to each other. The use of networked data in fraud detection becomes increasingly important to uncover fraudulent patterns and to detect in real-time when certain processes show some characteristics of irregular activities. Although analyses focus in the first place on fraud detection, the emphasis should shift towards fraud prevention, i.e. detecting fraud before it is even committed. As fraud is a time-evolving phenomenon, social network algorithms succeed to keep ahead of new types of fraud and to adapt to changing environment and surrounding effects.
Views: 8952 Bart Baesens
Social network analysis: Considerations for data collection and analysis
 
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Bernie Hogan completed his BA(hons) at the Memorial University of Newfoundland in Canada, where he received the University Medal in Sociology. Since then he has been working on Internet use and social networks at the University of Toronto under social network analysis pioneer Barry Wellman. Bernie received his Masters of Arts at Toronto in 2003, and defended his PhD Dissertation in the Fall of 2008. His dissertation examines how the use of ICTs alters the way people maintain their relationships in everyday life. In 2005 he was an intern at Microsoft’s Community Technologies Lab, working with Danyel Fisher on new models for email management. RESEARCH Bernie Hogan’s research focuses on the creation, maintenance and analysis of personal social networks, with a particular focus on the relation between online and offline networks. Hogan’s work has demonstrated the utility of visualization for network members, how the addition of new social media can complicate communication strategies, and how the uneven distribution of media globally can affect the ability of people to participate online. Currently, Hogan is working on techniques to simplify the deployment of personal network studies for newcomers as well as social-theoretical work on the relationship between naming conventions and identities. #datascienceclasses
Introduction to Social Network Analysis
 
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This workshop provides a broad overview of Social Network Analysis. In the first part of the workshop, a concise overview of theoretical concepts is provided, together with examples of data collection methods. The second section discusses network data analysis - network measurements (i.e. density, reciprocity, etc.) and node level measurements (i.e. degree centrality, betweenness centrality, etc.). The last part of the workshop introduces participants to UCINET and NetDraw, software packages used for data management, analysis and visualization.
Social Network Analysis - using Social Network Visualiser
 
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This video takes on the dreaded social network analysis. You may not use it in your assignment - but it might be worth your using in future life? See http://socnetv.sourceforge.net
Views: 7075 Micheal Axelsen