Interested in applying social network methods to better understand the structure of your business or organization?
In collaboration with Optimice, I will teach a workshop on Social Network Analysis for enterprises, organizations, and businesses using NodeXL.
Self-paced e-learning (4 hours)
Introduction to Social/Organisational Network Analysis
Network patterns and metrics
Software tools for network analysis
Managing an ONA Project
Module 1: Scoping your ONA Project (2 hour virtual session hosted by Patti Anklam)
Determining which business problem to solve with ONA
Review of case-studies
Determining your questions
Module 2: Setting up your ONA survey (2 hour virtual session hosted by Cai Kjaer / Laurence Lock Lee)
Setting up your survey
Working with mailing lists and other lists
Creating relationship sets and network questions
Previewing and launching the survey
Tracking progress and downloading responses
Module 3: Visualise networks with NodeXL (2 hour virtual session hosted by Marc Smith)
Getting started with NodeXL
Calculating and visualizing network metrics
Preparing data and filtering
Importing data from Social Media tools
Clustering and grouping
A number of ONA Practitioner Courses are available to suit the timezones of participants located in the US, Europe and/or Asia-Pacific (but not restricted to these regions):
Course Code
Date and Time
Time Zone
Payment
OPC-2012-9-EUR
29 February 2012 to 27 March 2012
(Registration deadline is 15 February 2012)Module 1: 13 March 2012 (10am – 12pm)
Module 2: 20 March 2012 (10am -12pm)
Module 3: 28 March 2012 (3 – 5pm)Self-paced to be completed before starting module 1.
Europe – London GMT
$US 1,599
OPC-2012-13-APAC
27 March 2012 to 25 April 2012
(Registration deadline is 13 March 2012)Module 1: 11 April 2012 (11am – 1pm)
Module 2: 18 April 2012 (11am – 1pm)
Module 3: 25 April 2012 (11am – 1pm)Self-paced to be completed before starting module 1.
Asia-Pacific – Sydney EST
$US 1,599
OPC-2012-17-US
25 April 2012 to 22 May 2012
(Registration deadline is 11 April 2012)Module 1: 8 May 2012 (4 – 6pm)
Module 2: 15 May 2012 (4 – 6pm)
Module 3: 22 May 2012 (4 – 6pm)Self-paced to be completed before starting module 1.
Crowds of people gather in social media around many products, services, businesses, and events but they can be difficult to see and understand. With new free and open tools, it is now possible to map and measure social media spaces, capturing the sub-groups and key people within and between them. Learn how to capture social media data and quickly generate a visual map of the crowd. With maps in hand, we will discuss ways they guide a journey to the key influencers and concepts in the crowd.
Description: Maps of the complex connections that form when people link, like, reply, rate, review, favorite, friend, follow, edit, and mention one another can reveal important trends. It is possible to create network maps with free and open tools that identify key people and sub-groups in any social media population with just a few key clicks. Can you make a pie chart? You can now make a network chart.
Abstract: Networks are a data structure common found across all social media services that allow populations to author collections of connections. The Social Media Research Foundation’s (http://www.smrfoundation.org) free and open NodeXL project (http://nodexl.codeplex.com) makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, Networks become as easy to create as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented.
We now live in a sea of tweets, posts, blogs, and updates coming from a significant fraction of the people in the connected world. Our personal and professional relationships are now made up as much of texts, emails, phone calls, photos, videos, documents, slides, and game play as by face-to-face interactions. Social media can be a bewildering stream of comments, a daunting fire hose of content. With better tools and a few key concepts from the social sciences, the social media swarm of favorites, comments, tags, likes, ratings, and links can be brought into clearer focus to reveal key people, topics and sub-communities. As more social interactions move through machine-readable data sets new insights and illustrations of human relationships and organizations become possible. But new forms of data require new tools to collect, analyze, and communicate insights.
My talk this year will focus on collecting and analyzing connections between digital objects (like users) and the insights these tools make possible.
Abstract: While digital content is archived in various ways, the “arcs” or links among people and their digital objects are not systematically saved. Efforts to store social media often overlooks including data about collections of connections. The Social Media Research Foundation is dedicated to open tools, open data, and open scholarship related to social media. It is producing tools that can collect, analyze and upload social media data, including the arcs that link people and objects. Using the free and open NodeXL application, users can collect, analyze and visualize complex networks and then upload the data to a growing archive on the web at NodeXLGraphGallery.org. As the group of researchers grows, an archive is being assembled to provide researchers around the world with the data about social media needed to understand the ways computer mediated communication tools shape society.
Mastering Social Media will give you practical tools on how to plan, execute and monitor your social media campaigns. Discussions will lead you through the introduction to social media marketing, understanding community dynamics, mapping social networks and applying network insights to your goals.
Brand Managers, Marketing Managers, Advertising Agencies, Digital Agencies, and PR Agencies are likely to find the day useful.
Venues and Dates
Cape Town
28 November 2011
Protea Hotel
Breakwater Lodge, Waterfront
Johannesburg
30 November 2011
Gordon Institute
of Business Science, Illovo
This map of connections among people who tweeted Teaparty starts on 11/15/2011 14:22 UTC and ends on 11/15/2011 17:23, a total of 3 hours and 1 minute of traffic.
The Teaparty data set contained 1,533 tweets, replies and mentions.
Blue edges are connections created by replies and mentions. Grey lines are follows relationships.
Top most between users:
@ronpaul
@michellemalkin
@christopherhull
@theteaparty_net
@capaction
@thedailyedge
@bill1phd
@dbargen
@gulagbound
@rightcandidates
Graph Metric: Value
Graph Type: Directed
Vertices: 659
Unique Edges: 8808
Edges With Duplicates: 1423
Total Edges: 10231
Self-Loops: 1084
Connected Components: 49
Single-Vertex Connected Components: 44
Maximum Vertices in a Connected Component: 606
Maximum Edges in a Connected Component: 10148
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance:2.693965
Graph Density: 0.02036797
NodeXL Version: 1.0.1.193
The major clusters are composed of teaparty supporters. The center bottom cluster are teaparty critics.
This map of the connections among people who tweeted Occupywallstreet starts on 11/15/2011 23:08 and ends on 11/15/2011 23:34 UTC, a total of 26 minutes of traffic.
Occupywallstreet 1,370 tweets, replies and mentions
Blue edges are connections created by replies and mentions. Grey lines are follows relationships.
Top most between users:
@occupywallst
@mmflint
@nyclu
@allisonkilkenny
@andrewbreitbart
@operationleaks
@occupydenver
@theatlantic
@usgeneralstrike
@rt_com
Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 3546
Edges With Duplicates: 826
Total Edges: 4372
Self-Loops: 794
Connected Components: 241
Single-Vertex Connected Components: 230
Maximum Vertices in a Connected Component: 747
Maximum Edges in a Connected Component: 3998
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.65438
Graph Density: 0.003246246
NodeXL Version: 1.0.1.194
Some notable contrasts:
Teaparty Graph Density: 0.002652645
Occupywallstreet Graph Density: 0.02036797 – significantly lower levels of interconnection
Teaparty: Single-Vertex Connected Components 44 of 1000
Occupywallstreet: Single-Vertex Connected Components 283 of 1000
Many more “isolates” (Single-Vertex Connected Components) in Occupywallstreet.
Many more hubs, and more retweeting activity in Occupywallstreet.
The difference in duration of these data sets illustrates the relative speed of content creation in the topics. The data sets are commensurable in that they are both the result of a single query against the Twitter search API. So both maps are the results of charting connections among the authors of the last 1500 tweets, how ever long that takes to create.
Course Summary: Networks are everywhere in the natural and social world. New tools are making the task of getting, processing, measuring, visualizing and gaining insights from network data sets easier than ever before. The rise of social media offers a new and abundant source of network data. The NodeXL project (http://www.codeplex.com/nodexl) from the Social Media Research Foundation (http://www.smrfoundation.org) offers a free and open path to network overview, discovery and exploration within the context of the familiar Excel spreadsheet. In this short course we will introduce the NodeXL application and review the landscape of networks, social networks, and social media networks. Using the tool, non-programmers can quickly select a network of interest from various social media and other data sources. Twitter, flickr, YouTube, email, the World Wide Web, and Facebook data can be quickly imported into NodeXL. Networks can then be analyzed and visualized using tools similar to those used to create a pie chart or line graph [1]. As the challenge and cost of network acquisition and analysis drops, abundant data sets are being generated that document the range of variation of diverse sources of social media. How many different kinds of Twitter hashtags exist? Using snapshots of hundreds of hashtags collected over a year, it is now possible to build rough taxonomies of this kind of social media. NodeXL provides access to a web gallery of data [2], allowing users to browse existing data sets and upload their own as well. Borrowing the vision of telescope arrays that create composite images far better than any individual instrument could, the Social Media Research Foundation envisions an user generated archive that provides a research asset that supports the collective effort to understand the structures and dynamics of network data.
Course Objectives
After this course, participants will:
(1) Be familiar with the basic concepts of networks, social networks and social media networks
(2) Understand the core features of the NodeXL network analysis and visualization tool
(3) Review images and data sets for dozens of different social media networks
(4) Learn to identify general types of social media networks along with the key people and groups within them
Target Audience
This course is suitable for people with some experience or interest in social media, social science, or social network analysis. It is particularly appropriate for those who are involved in studying social structures and their change over time.
Laboratory and IT requirements:
Participants will need access to a computer connected to the Internet and will be supplied with the free NodeXL software.
Date: 2011 November 3 (Thurs)
Place: COEX Grand Ballroom, Seoul, Korea
Organized by Advanced Institutes of Convergence Technologies (AICT), Seoul National University (SNU)
In Cooperation with Ministry of Knowledge Economy, Ministry of Education, Science and Technology, National Research Foundation of Korea, Graduate School of Convergence Science and Technology (GSCST)
Symposium Chair : Choi, Yanghee (President, AICT)
Program
09:00~09:30 Registration
09:30~10:00 Opening Ceremony
Plenary Session : Smart & Humane World through Convergence
10:00~10:40 Speaker (TBD)
10:50~11:30 Speaker (TBD)
11:30~13:00 Lunch
Session 1 : Bio Convergence (Chair : Prof. Kim, Sunghoon)
Session 2 : IT Convergence (Chair : Dr. Lee, Manjai)
Session 3 : Appropriate Technology (Chair : Prof. Kang, Namjun)
13:00~15:00 Scott A. Strobel (Professor, Yale University)
Speaker Kevin Kim (Professor, University of Illinois)
Speaker Masaru Kitsuregawa (Professor, Tokyo University)
Speaker Haesun Park (Professor, Georgia Institute of Technology)
Speaker Marc Smith (Connected Action)
Speaker Sang-goo Lee (Professor, Seoul National University)
Speaker Haklae Kim (Samsung)
Speaker Raghu Ramakrishnan (Yahoo)
Abstract: Networks are everywhere except the end user desktop. NodeXL, the free and open network overview, discovery and exploration add-in for the popular and familiar Excel (2007/2010) spreadsheet allows users who are comfortable making pie charts to now make useful network visualizations. Developed and released by the Social Media Research Foundation, NodeXL uses Excel as a framework, providing a GUI network browser (a “web browser”?) that novices can use quickly and experts can use to generate sophisticated results. Data importers provide access to a range of social media network data sources like Twitter, flickr, YouTube, Facebook, email, the WWW, and more through standard file formats (CSV, GraphML, Matrix). Simple to use tools can automatically analyze, visualize and highlight insights in complex network graphs. Using NodeXL, researchers have been collecting a wide range of network data sets from various social media services. These images reveal a range of common social formations in social media and point to people who occupy strategic locations in these graphs.
This is a map of the connections among the people who tweeted the term “PAWCON” on the first day of the event:
These are the connections among the Twitter users who recently tweeted the word #pawcon when queried on October 19, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
Top most between users:
@tapan_patel
@pawcon
@sasanalytics
@deloitteba
@kristinevick
@jamet123
@zementis
@kdnuggets
@tibcospotfire
@saspublishing
Graph Metric: Value
Graph Type: Directed
Vertices: 41
Unique Edges: 233
Edges With Duplicates: 120
Total Edges: 353
Self-Loops: 44
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 40
Maximum Edges in a Connected Component: 352
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 1.87133
Graph Density: 0.15304878
NodeXL Version: 1.0.1.179
Here is an example map of the connections among the people who tweeted the term “pawcon” in Twitter on September 14th, a week prior to the event.
Manu Sharma, Principle Research Scientist at LinkedIn gave a great presentation on the patterns found in their data. Big data, for example, showed that most of the people who previously worked at recently failed banks and financial institutions have updated their profiles to show that they mostly have new jobs at some of the remaining companies in the industry.
The Social Media Research Foundation is pleased to announce the immediate availability of ThreadMill 0.1. ThreadMill is a free and open application that consumes message thread data and produces reports about each author, thread, forum, and board along with visualizations of the patterns of connection and activity. ThreadMill is written in Ruby, and depends on MongoDB, SinatraRB, HAML, and Flash to collect, analyze, and report data about collections of conversation threads.
Threaded conversations are a major form of social media. Message boards, email and email lists, twitter, blog comments, text messages, and discussion forums are all social media systems built around the message thread data structure. As messages are exchanged through these systems, some messages are sent as a reply to a particular previous message. As messages are sent in reply to prior messages, chains of messages form. Message chains come in two major forms: branching and non-branching. Branching threads are those that allow more than one message to reply to a prior message. Non-branching threads are single chains, like a string of pearls, that allow only one message to reply to a prior message. Many web based message boards are non-branching. Many email systems and discussion forums are branching.
ThreadMill requires a minimal set of data elements to generate its reports. A data table must minimally have a column of information for each message that includes the name of the message board, the forum, the thread, and the author, along with a unique identifier for each message and the date and time it was posted. Optional data elements include the unique identifier of the message being replied to, the URL of the message, and the URL for a profile photo.
All forms of threaded message exchange can be measured. Simple measures like the count of the number of messages or the number of authors are obvious and useful. Other measures can be created from more sophisticated analysis. For example, the network of connections that forms as different authors reply to one another can be extracted and analyzed using network analysis methods. It is possible to calculate metrics from these networks of reply that describe the location of each person in the graph.
ThreadMill generates several data sets that can be used to create visualizations of the activity and structure of a message board collection.
A Treemap data set can illustrate the hierarchy of encapsulated authors within threads, threads within fora, fora within boards, and boards within collections. Treemap visualizations of collections of threaded conversations can quickly highlight the most active or populous discussions.
An AuthorLine visualization takes the form of a double histogram, with bubbles representing each thread active in each time period sized by the volume of messages the author contributed, sorted by size. Threads that have been initiated by the author are represented as bubbles above the center line. Messages that the author contributes to threads started by other authors are represented as bubbles stacked below the center line. AuthorLines quickly reveal the pattern of activity an author displays and identifies which of several types of contributors the author is.
A scatter plot visualization represents each author as a bubble in an X-Y space defined by the number of different days the author was active against the average number of messages the author contributes to the threads in which they participate.
A time series line chart reveals the days of maximum and minimum activity along with trends.
A network diagram reveals the overall structure of the discussion space and the people who occupy strategic locations within the network graph.
ThreadMill has received generous assistance from Morningside Analytics. Bruce Woodson implemented ThreadMill.
These are the connections among the Twitter users who recently tweeted the word #JW11 when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
These are the connections among the Twitter users who recently tweeted the word AOIR when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
Graph Metric: Value
Graph Type: Directed
Vertices: 98
Unique Edges: 119
Edges With Duplicates: 672
Total Edges: 791
Self-Loops: 153
Connected Components: 40
Single-Vertex Connected Components: 35
Maximum Vertices in a Connected Component: 55
Maximum Edges in a Connected Component: 710
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.257477
Graph Density: 0.035766884
NodeXL Version: 1.0.1.179
By expanding the query to include #IR12, the conference hashtag, the network expands to include:
Connections among the Twitter users who recently tweeted the word AOIR OR #IR12 when queried on October 11, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
![Impala, Sabi Sands Game Preserve, Limpopo, South Africa](http://farm8.static.flickr.com/7156/6465699563_30e7fe067b_t.jpg "The markings on their haunches make an "M" - like the McDonalds Logo, this tells the lions where to eat.")
![20111019-NodeXL-Twitter-pawcon graph](http://farm7.static.flickr.com/6053/6261006732_505e6f87d8.jpg "From: <a href=\"http://www.connectedaction.net\" rel=\"nofollow\">www.connectedaction.net</a>
Link: <a href=\"http://www.flickr.com/photos/marc_smith/6261006732/\">www.flickr.com/photos/marc_smith/6261006732/</a>
These are the connections among the Twitter users who recently tweeted the word <a href=\"http://search.twitter.com/search?q= #pawcon\" rel=\"nofollow\"> #pawcon</a> when queried on October 19, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
See: <a href=\"http://www.predictiveanalyticsworld.com/newyork/2011/agenda_overview.php\" rel=\"nofollow\">www.predictiveanalyticsworld.com/newyork/2011/agenda_over...</a>
Layout using the "Group Layout" composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.
A larger version of the image is here: <a href=\"http://www.flickr.com/photos/marc_smith/6261006732/sizes/l/in/photostream/\">www.flickr.com/photos/marc_smith/6261006732/sizes/l/in/ph...</a>
Betweenness Centrality is defined here: <a href=\"http://en.wikipedia.org/wiki/Centrality#Betweenness_centrality\" rel=\"nofollow\">en.wikipedia.org/wiki/Centrality#Betweenness_centrality</a>
Clauset-Newman-Moore algorithm is defined here: <a href=\"http://pre.aps.org/abstract/PRE/v70/i6/e066111\" rel=\"nofollow\">pre.aps.org/abstract/PRE/v70/i6/e066111</a>
Top most between users:
@tapan_patel
@pawcon
@sasanalytics
@deloitteba
@kristinevick
@jamet123
@zementis
@kdnuggets
@tibcospotfire
@saspublishing
Graph Metric: Value
Graph Type: Directed
Vertices: 41
Unique Edges: 233
Edges With Duplicates: 120
Total Edges: 353
Self-Loops: 44
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 40
Maximum Edges in a Connected Component: 352
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 1.87133
Graph Density: 0.15304878
NodeXL Version: 1.0.1.179
More NodeXL network visualizations are here: <a href=\"http://www.flickr.com/photos/marc_smith/sets/72157622437066929/\">www.flickr.com/photos/marc_smith/sets/72157622437066929/</a>
NodeXL is free and open and available from <a href=\"http://www.codeplex.com/nodexl\" rel=\"nofollow\">www.codeplex.com/nodexl</a>
NodeXL is developed by the <a href=\"http://www.smrfoundation.org\" rel=\"nofollow\">Social Media Research Foundation</a> (<a href=\"http://www.smrfoundation.org\" rel=\"nofollow\">www.smrfoundation.org</a>) - which is dedicated to open tools, open data, and open scholarship.
The book, <a href=\"http://www.amazon.com/gp/product/0123822297?ie=utf8&tag=conneactio-20&linkcode=as2&camp=1789&creative=390957&creativeasin=0123822297\" rel=\"nofollow\">Analyzing social media networks with NodeXL: Insights from a connected world</a>, is available from Morgan Kaufmann and from Amazon.
<a href=\"http://twitter.com/#!/marc_smith\" rel=\"nofollow\">Marc Smith</a> on <a href=\"http://twitter.com/#!/marc_smith\" rel=\"nofollow\">Twitter</a>.")
![20111010-NodeXL-Twitter-#jw11 graph](http://farm7.static.flickr.com/6231/6234653454_083947bb6b.jpg "From: <a href=\"http://www.connectedaction.net\" rel=\"nofollow\">www.connectedaction.net</a>
Link: <a href=\"http://www.flickr.com/photos/marc_smith/6234653454/\">www.flickr.com/photos/marc_smith/6234653454/</a>
Connections among the Twitter users who recently tweeted the word <a href=\"http://search.twitter.com/search?q=#JW11\" rel=\"nofollow\">#JW11</a> when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
See: <a href=\"http://www.jivesoftware.com/jiveworld\" rel=\"nofollow\">www.jivesoftware.com/jiveworld</a>
Layout using the "Group Layout" composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.
A larger version of the image is here: <a href=\"http://www.flickr.com/photos/marc_smith/6234653454/sizes/l/in/photostream/\">www.flickr.com/photos/marc_smith/6234653454/sizes/l/in/ph...</a>
Betweenness Centrality is defined here: <a href=\"http://en.wikipedia.org/wiki/Centrality#Betweenness_centrality\" rel=\"nofollow\">en.wikipedia.org/wiki/Centrality#Betweenness_centrality</a>
Clauset-Newman-Moore algorithm is defined here: <a href=\"http://pre.aps.org/abstract/PRE/v70/i6/e066111\" rel=\"nofollow\">pre.aps.org/abstract/PRE/v70/i6/e066111</a>
Top most between users:
<a href=\"https://twitter.com/#!/\" rel=\"nofollow\">@jivesoftware</a>
<a href=\"https://twitter.com/#!/gialyons\" rel=\"nofollow\">@gialyons</a>
<a href=\"https://twitter.com/#!/mikefraietta\" rel=\"nofollow\">@mikefraietta</a>
<a href=\"https://twitter.com/#!/cflanagan\" rel=\"nofollow\">@cflanagan</a>
<a href=\"https://twitter.com/#!/cosmopolitan_lv\" rel=\"nofollow\">@cosmopolitan_lv</a>
<a href=\"https://twitter.com/#!/ginorossi\" rel=\"nofollow\">@ginorossi</a>
<a href=\"https://twitter.com/#!/kristinhersant\" rel=\"nofollow\">@kristinhersant</a>
<a href=\"https://twitter.com/#!/thebrandbuilder\" rel=\"nofollow\">@thebrandbuilder</a>
<a href=\"https://twitter.com/#!/alanlepo\" rel=\"nofollow\">@alanlepo</a>
<a href=\"https://twitter.com/#!/mor_trisha\" rel=\"nofollow\">@mor_trisha</a>
Graph Metric: Value
Graph Type: Directed
Vertices: 345
Unique Edges: 3606
Edges With Duplicates: 2072
Total Edges: 5678
Self-Loops: 632
Connected Components: 11
Single-Vertex Connected Components: 9
Maximum Vertices in a Connected Component: 334
Maximum Edges in a Connected Component: 5659
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.353001
Graph Density: 0.034184361
NodeXL Version: 1.0.1.179
More NodeXL network visualizations are here: <a href=\"http://www.flickr.com/photos/marc_smith/sets/72157622437066929/\">www.flickr.com/photos/marc_smith/sets/72157622437066929/</a>
NodeXL is free and open and available from <a href=\"http://www.codeplex.com/nodexl\" rel=\"nofollow\">www.codeplex.com/nodexl</a>
NodeXL is developed by the <a href=\"http://www.smrfoundation.org\" rel=\"nofollow\">Social Media Research Foundation</a> (<a href=\"http://www.smrfoundation.org\" rel=\"nofollow\">www.smrfoundation.org</a>) - which is dedicated to open tools, open data, and open scholarship.
The book, <a href=\"http://www.amazon.com/gp/product/0123822297?ie=utf8&tag=conneactio-20&linkcode=as2&camp=1789&creative=390957&creativeasin=0123822297\" rel=\"nofollow\">Analyzing social media networks with NodeXL: Insights from a connected world</a>, is available from Morgan Kaufmann and from Amazon.
<a href=\"http://twitter.com/#!/marc_smith\" rel=\"nofollow\">Marc Smith</a> on <a href=\"http://twitter.com/#!/marc_smith\" rel=\"nofollow\">Twitter</a>.")
![20111010-NodeXL-Twitter-aoir graph](http://farm7.static.flickr.com/6237/6233561108_45c5f3661c.jpg "From: <a href=\"http://www.connectedaction.net\" rel=\"nofollow\">www.connectedaction.net</a>
Link: <a href=\"http://www.flickr.com/photos/marc_smith/6233561108\">www.flickr.com/photos/marc_smith/6233561108</a>
Connections among the Twitter users who recently tweeted the word <a href=\"http://search.twitter.com/search?q=AOIR\" rel=\"nofollow\">AOIR</a> when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
Updated map at: <a href=\"http://www.flickr.com/photos/marc_smith/6234691231\">www.flickr.com/photos/marc_smith/6234691231</a>
See: <a href=\"http://ir12.aoir.org/\" rel=\"nofollow\">ir12.aoir.org/</a>
Layout using the "Group Layout" composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.
A larger version of the image is here: <a href=\"http://www.flickr.com/photos/marc_smith/6233561108/sizes/l/in/photostream/\">www.flickr.com/photos/marc_smith/6233561108/sizes/l/in/ph...</a>
Betweenness Centrality is defined here: <a href=\"http://en.wikipedia.org/wiki/Centrality#Betweenness_centrality\" rel=\"nofollow\">en.wikipedia.org/wiki/Centrality#Betweenness_centrality</a>
Clauset-Newman-Moore algorithm is defined here: <a href=\"http://pre.aps.org/abstract/PRE/v70/i6/e066111\" rel=\"nofollow\">pre.aps.org/abstract/PRE/v70/i6/e066111</a>
Top most between users:
<a href=\"https://twitter.com/#!/ir12\" rel=\"nofollow\">@ir12</a>
<a href=\"https://twitter.com/#!/barrywellman\" rel=\"nofollow\">@barrywellman</a>
<a href=\"https://twitter.com/#!/mikemonello\" rel=\"nofollow\">@mikemonello</a>
<a href=\"https://twitter.com/#!/netcrit\" rel=\"nofollow\">@netcrit</a>
<a href=\"https://twitter.com/#!/andresmh\" rel=\"nofollow\">@andresmh</a>
<a href=\"https://twitter.com/#!/s_t_e_v_e_jones\" rel=\"nofollow\">@s_t_e_v_e_jones</a>
<a href=\"https://twitter.com/#!/zizip\" rel=\"nofollow\">@zizip</a>
<a href=\"https://twitter.com/#!/kellybergstrom\" rel=\"nofollow\">@kellybergstrom</a>
<a href=\"https://twitter.com/#!/katypearce\" rel=\"nofollow\">@katypearce</a>
<a href=\"https://twitter.com/#!/guillaumelatzko\" rel=\"nofollow\">@guillaumelatzko</a>
Graph Metric: Value
Graph Type: Directed
Vertices: 98
Unique Edges: 119
Edges With Duplicates: 672
Total Edges: 791
Self-Loops: 153
Connected Components: 40
Single-Vertex Connected Components: 35
Maximum Vertices in a Connected Component: 55
Maximum Edges in a Connected Component: 710
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.257477
Graph Density: 0.035766884
NodeXL Version: 1.0.1.179
More NodeXL network visualizations are here: <a href=\"http://www.flickr.com/photos/marc_smith/sets/72157622437066929/\">www.flickr.com/photos/marc_smith/sets/72157622437066929/</a>
NodeXL is free and open and available from <a href=\"http://www.codeplex.com/nodexl\" rel=\"nofollow\">www.codeplex.com/nodexl</a>
NodeXL is developed by the <a href=\"http://www.smrfoundation.org\" rel=\"nofollow\">Social Media Research Foundation</a> (<a href=\"http://www.smrfoundation.org\" rel=\"nofollow\">www.smrfoundation.org</a>) - which is dedicated to open tools, open data, and open scholarship.
The book, <a href=\"http://www.amazon.com/gp/product/0123822297?ie=utf8&tag=conneactio-20&linkcode=as2&camp=1789&creative=390957&creativeasin=0123822297\" rel=\"nofollow\">Analyzing social media networks with NodeXL: Insights from a connected world</a>, is available from Morgan Kaufmann and from Amazon.
<a href=\"http://twitter.com/#!/marc_smith\" rel=\"nofollow\">Marc Smith</a> on <a href=\"http://twitter.com/#!/marc_smith\" rel=\"nofollow\">Twitter</a>.")
![20111011-NodeXL-Twitter-aoir OR #IR12 graph](http://farm7.static.flickr.com/6151/6234691231_5f159c6a12.jpg "From: <a href=\"http://www.connectedaction.net\" rel=\"nofollow\">www.connectedaction.net</a>
Link: <a href=\"http://www.flickr.com/photos/marc_smith/6233561108\">www.flickr.com/photos/marc_smith/6233561108</a>
Connections among the Twitter users who recently tweeted the word <a href=\"http://search.twitter.com/search?q=AOIR OR #IR12\" rel=\"nofollow\">AOIR OR #IR12</a> when queried on October 11, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
See: <a href=\"http://ir12.aoir.org/\" rel=\"nofollow\">ir12.aoir.org/</a>
Layout using the "Group Layout" composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.
A larger version of the image is here: <a href=\"http://www.flickr.com/photos/marc_smith/6233561108/sizes/l/in/photostream/\">www.flickr.com/photos/marc_smith/6233561108/sizes/l/in/ph...</a>
Betweenness Centrality is defined here: <a href=\"http://en.wikipedia.org/wiki/Centrality#Betweenness_centrality\" rel=\"nofollow\">en.wikipedia.org/wiki/Centrality#Betweenness_centrality</a>
Clauset-Newman-Moore algorithm is defined here: <a href=\"http://pre.aps.org/abstract/PRE/v70/i6/e066111\" rel=\"nofollow\">pre.aps.org/abstract/PRE/v70/i6/e066111</a>
Top most between users:
<a href=\"https://twitter.com/#!/ir12\" rel=\"nofollow\">@ir12</a>
<a href=\"https://twitter.com/#!/barrywellman\" rel=\"nofollow\">@barrywellman</a>
<a href=\"https://twitter.com/#!/mikemonello\" rel=\"nofollow\">@mikemonello</a>
<a href=\"https://twitter.com/#!/netcrit\" rel=\"nofollow\">@netcrit</a>
<a href=\"https://twitter.com/#!/andresmh\" rel=\"nofollow\">@andresmh</a>
<a href=\"https://twitter.com/#!/s_t_e_v_e_jones\" rel=\"nofollow\">@s_t_e_v_e_jones</a>
<a href=\"https://twitter.com/#!/zizip\" rel=\"nofollow\">@zizip</a>
<a href=\"https://twitter.com/#!/kellybergstrom\" rel=\"nofollow\">@kellybergstrom</a>
<a href=\"https://twitter.com/#!/katypearce\" rel=\"nofollow\">@katypearce</a>
<a href=\"https://twitter.com/#!/guillaumelatzko\" rel=\"nofollow\">@guillaumelatzko</a>
Graph Metric: Value
Graph Type: Directed
Vertices: 231
Unique Edges: 1984
Edges With Duplicates: 2118
Total Edges: 4102
Self-Loops: 586
Connected Components: 46
Single-Vertex Connected Components: 40
Maximum Vertices in a Connected Component: 181
Maximum Edges in a Connected Component: 4012
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.213887
Graph Density: 0.046320346
NodeXL Version: 1.0.1.179
More NodeXL network visualizations are here: <a href=\"http://www.flickr.com/photos/marc_smith/sets/72157622437066929/\">www.flickr.com/photos/marc_smith/sets/72157622437066929/</a>
NodeXL is free and open and available from <a href=\"http://www.codeplex.com/nodexl\" rel=\"nofollow\">www.codeplex.com/nodexl</a>
NodeXL is developed by the <a href=\"http://www.smrfoundation.org\" rel=\"nofollow\">Social Media Research Foundation</a> (<a href=\"http://www.smrfoundation.org\" rel=\"nofollow\">www.smrfoundation.org</a>) - which is dedicated to open tools, open data, and open scholarship.
The book, <a href=\"http://www.amazon.com/gp/product/0123822297?ie=utf8&tag=conneactio-20&linkcode=as2&camp=1789&creative=390957&creativeasin=0123822297\" rel=\"nofollow\">Analyzing social media networks with NodeXL: Insights from a connected world</a>, is available from Morgan Kaufmann and from Amazon.
<a href=\"http://twitter.com/#!/marc_smith\" rel=\"nofollow\">Marc Smith</a> on <a href=\"http://twitter.com/#!/marc_smith\" rel=\"nofollow\">Twitter</a>.")
