This is the collection of keyword pairs that appeared in two clusters of people who Tweeted about “Paul Ryan”, the Republican Congressman from Wisconsin who delivered the GOP rebuttal to the 2011 United States State of the Union Address.  This network illustrates the ways that certain word pairs appears only or predominantly in one cluster (colored here Red and Blue) or the other. Terms that appeared in both clusters appear as purple.

Social networks are built from relationships between people.  Keyword networks are built from relationships between words and other text strings.  When two words appear in the same message, sentence, or alongside one another ties of different strengths are created.  The networks that result can illuminate the relationships among topics of importance in a collection of messages.

Markus Strohmaier from the Technical University Graz (TUG) along with Claudia Wagner gave us inspiration in a paper:

C. Wagner, M. Strohmaier, The Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from Social Awareness Streams, Semantic Search 2010 Workshop (SemSearch2010), in conjunction with the 19th International World Wide Web Conference (WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010. (pdf)

in which they defined a range of ways two words (technically these are strings, they may not really be words) can be associated with one another.  Words could be linked if they are in the same tweet, next to one another, or sequential among other ways to link terms.

NodeXL has not had any features for exploring the networks in texts.  Now with the addition of a new macro from Scott Golder, it is fairly simple to extract pairs of keywords from collection of tweets.  NodeXL’s Twitter importer can optionally include the content of the tweet that included the search term and this column of text can now be processed itself into a new network based on the ways words appear together in tweets.

This feature builds on the work of several people.  Scott Golder from Cornell started the ball rolling with a simple but effective VBA script that allowed others to build and refine the models of what counts as a tie between two words.  Vladimir Barash added several refinements including support for stop word lists to remove common terms.  Scott then picked up the code again and added a set of features for selecting the nature of the graph and making it easier to select the options needed.

The code for the Keyword Network macro is below.

The instructions to use it take a few steps to complete:

1. Create a new workbook, eg a list of tweets or an import from a Twitter search, whatever. Save it as .xlsm. The m is important. This can be an existing NodeXL workbook.

2. Go to Developer -> Macros. Make up a name; it doesn’t matter because it’ll get overwritten. Then press Create. the VBA window will open.

3. In the big text are that says “Sub whatever() End Sub”, select all that text and delete it. Paste in the contents of the text file below.

4. Go to Tools->Reference. Check the checkboxes for “Microsoft Scripting Runtime” and “Microsott VBScript Regular Expressions”. Press OK. Save the file (File->Save) then exit (“Close and return to Microsoft Excel”).

5. Now go to Developer -> Macros. Choose CreateWordNet and press the Run button.

6. It’ll ask you for a worksheet name, a column and a start-row. Then it’ll create a new worksheet with the edgelist in it.

The edge list is not directed (there isn’t really a concept of direction in “co-occurs”) but is weighted. Each pair is weighted by the number of times it appears.

This version also includes options for edge creation.

First, it is now possible to suppress edges of weight=1, which is helpful in getting rid of a lot of garbage.

Second, it is now possible to defined edges by adjacency or co-tweeting. Given a tweet of words “w1 w2 w3″ adjacency will give edges w1-w2 and w2-w3, while co-tweeting will give edges w1-w2, w1-w3, w2-w3.

For edges defined by adjacency, you may choose directed or undirected edges. So a tweet of “Marc Smith Marc” (for example) would generate the weighted directed edges Marc,Smith,1 and Smith,Marc,1 while the sole undirected edge would be Marc,Smith,2. That is, for undirected edges (where ordering doesn’t matter) the words are alphabetized.

Create Word Network VB Macro Vdb5

I will speak this March 4th at the 2012 Predictive Analytics World in San Francisco about ” Crowd Photography for Social Media“.

http://www.predictiveanalyticsworld.com/sanfrancisco/2012/speakers.php
http://www.predictiveanalyticsworld.com/sanfrancisco/2012/agenda.php#

Monday @ 5:25-5:45pm

Track 1:
Social Data Case Study:
Social Media Research Foundation
Crowd Photography for Social Media

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.

Speaker: Marc Smith, Director, Social Media Research Foundation

Mapping social media networks (with no coding) using NodeXL

Time: 16:50 on 01 Mar 2012.

Session type: 40 minute presentation

Topics: Visualization & Interface

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.

I will present a talk at Personal Digital Archiving 2012 titled “Arc-chiving: saving social links for study“.

The conference will be held on Thursday-Friday, February 23-24, 2012 at the Internet Archive in San Francisco.

News and updates on the conference will be posted at the conference web site, http://personalarchiving.com.

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.

My talk at the 2011 Personal Digital Archiving conference is available through the Internet Archive’s video service:

Mastering Social Media 2011 is a workshop scheduled for November 28 and 30 in Cape Town and Johannesburg, South Africa.

My partner, Walter Pike, is a Marketing Maven and Founder of PiKE | New Marketing (www.pike.co.za) and the founder of the Digital Academy www.digitalacademy.co.za
He blogs at walterpike.com.

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

Johannesburg

30 November 2011
Gordon Institute
of Business Science,
Illovo

Pictures:

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.

Methodologies for Web and Social Media Data Analysis in Social Science and Policy Research

CCSR Short Course
Social Media Network Analysis using NodeXL

November 9th  9.00 am – 5.30 pm.

Marc Smith
Social Media Research Foundation
http://www.smrfoundation.org

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.

[1] NodeXL Image Gallery: http://www.flickr.com/photos/marc_smith/sets/72157622437066929/
[2] NodeXL Graph Gallery: http://nodexlgraphgallery.org

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.

Suggested Reading
Analyzing social media networks with NodeXL: Insights from a connected world
http://www.amazon.com/gp/product/0123822297?ie=UTF8&tag=conneactio-20&linkCode=as2&camp=1789&creative=390957&creativeASIN=0123822297

EventGraphs:
http://www.cs.umd.edu/localphp/hcil/tech-reports-search.php?number=2010-13

Visualizing the Signatures of Social Roles in Online Discussion Groups:
http://www.cmu.edu/joss/content/articles/volume8/Welser/

Discussion catalysts in online political discussions: Content importers and conversation starters
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-JCMC-Discussion-Catalysts-Himelboim-and-Smith.pdf

Analyzing (Social Media) Networks with NodeXL
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-CT-NodeXL-and-Social-Queries-a-social-media-network-analysis-toolkit.pdf

Whiter the experts: Social affordances and the cultivation of experts in community Q&A systems
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-Social-Computing-Whither-the-Experts.pdf

First steps to NetViz Nirvana: evaluating social network analysis with NodeXL
http://www.cs.umd.edu/~cdunne/pubs/Bonsignore09Firststepsto.pdf

]]> http://www.connectedaction.net/2011/11/05/november-8-2011-university-of-manchester-nodexl-sna-social-media-workshop/feed/ 0 http://www.connectedaction.net/2011/10/28/event-localsocialsummit11-and-social-media-research-foundation-reception-november-10th-2011/?utm_source=rss&utm_medium=rss&utm_campaign=event-localsocialsummit11-and-social-media-research-foundation-reception-november-10th-2011 http://www.connectedaction.net/2011/10/28/event-localsocialsummit11-and-social-media-research-foundation-reception-november-10th-2011/#comments Fri, 28 Oct 2011 08:00:30 +0000 Marc Smith http://www.connectedaction.net/?p=5260 & 

On November 9th and 10th, 2011 the LocalSocialSummit11 will be held in London.

On Thursday morning, November 10th, Doctor Bernie Hogan from the Oxford Internet Institute and member of the Social Media Research Foundation will speak on the topic:

Insights: Social Network Analysis On Facebook Data, with a local twist

Thursday at 3:45 I will speak at the event on:

Charting Collections Of Connections In Social Media

Following the conference from 5 to 6pm will be a reception for the Social Media Research Foundation.

Please join us for refreshments and a discussion of the ways social media analysis tools can guide your engagement with stakeholders around the world.

The event is held at wallacespace:

22 Duke’s Road, London, WC1H 9PN
t +44(0)20 7395 1265
f +44 (0)20 7836 9591
e ask@wallacespace.com

22 Duke’s Road is only minutes walk away from Euston, St Pancras International, King’s Cross and Russell Square Tube.

Registration form for attending the event is below….

I will speak at the International Symposium on Convergence Technology (ConTech 2011) – Smart & Humane World – on November 3rd in Seoul, South Korea.

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)

My slides: 20111103 con tech2011-marc smith

I will also visit Professor Han Woo Park at YeungNam University (Wikipedia) in Daegu, South Korea to meet with his students in the Webometrics Institute program.

This will be my second trip to Korea, I was there last year for a related event.  Pictures after the jump:

I presented on social media network analysis on October 20, 2011 in New York City at Predictive Analytics World.

A map of the connections among the people tweeting about the #Pawcon hashtag is below.

Network Maps for End Users: Collect, Analyze, Visualize and Communicate Network Insights with Zero Coding

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.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6261006732/sizes/l/in/ph…

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 event was held at the New York Hilton: Maps & Directions

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.

See: www.jivesoftware.com/jiveworld

A larger version of the image is here: www.flickr.com/photos/marc_smith/6234653454/sizes/l/in/ph…

Top most between users:
@jivesoftware
@gialyons
@mikefraietta
@cflanagan
@cosmopolitan_lv
@ginorossi
@kristinhersant
@thebrandbuilder
@alanlepo
@mor_trisha

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: www.flickr.com/photos/marc_smith/sets/72157622437066929/

http://ir12.aoir.org/

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.

See: ir12.aoir.org/

Top most between users:
@ir12
@barrywellman
@mikemonello
@netcrit
@andresmh
@s_t_e_v_e_jones
@zizip
@kellybergstrom
@katypearce
@guillaumelatzko

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.

Top most between users:
@ir12
@barrywellman
@mikemonello
@netcrit
@andresmh
@s_t_e_v_e_jones
@zizip
@kellybergstrom
@katypearce
@guillaumelatzko

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: www.flickr.com/photos/marc_smith/sets/72157622437066929/

Here is the updated map of connections among people who recently tweeted #occupywallstreet:

These are the connections among the Twitter users who recently tweeted the word OccupyWallStreet 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: occupywallst.org/

A larger version of the image is here: www.flickr.com/photos/marc_smith/6232030871/sizes/l/in/ph…

Top most between users:
@ajenglish
@anonymousirc
@cnbc
@rawstory
@ryannewyork
@mediabistro
@blogdiva
@brentnhunter
@krenner2
@rebuilddream

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 3014
Edges With Duplicates: 720
Total Edges: 3734
Self-Loops: 994
Connected Components: 318
Single-Vertex Connected Components: 300
Maximum Vertices in a Connected Component: 664
Maximum Edges in a Connected Component: 3326
Maximum Geodesic Distance (Diameter): 9
Average Geodesic Distance: 3.508133
Graph Density: 0.002524525
NodeXL Version1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

Link: www.flickr.com/photos/marc_smith/6225446144/

Each user is scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: occupywallst.org/

A larger version of the image is here: www.flickr.com/photos/marc_smith/6225446144/sizes/l/in/ph…

Top most between users:
@maddow
@motherjones
@aclu
@zerohedge
@mmfa
@spread4freedom
@diggrbiii
@angelsavant
@marychastain
@katiepavlich

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 3835
Edges With Duplicates: 916
Total Edges: 4751
Self-Loops: 1109
Connected Components: 311
Single-Vertex Connected Components: 301
Maximum Vertices in a Connected Component: 678
Maximum Edges in a Connected Component: 4327
Maximum Geodesic Distance (Diameter): 9
Average Geodesic Distance: 3.283708
Graph Density: 0.003408408
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

The Future of Technology in Education workshop met on 7 October 2011 in London at the University of London Computer Centre (ULCC) to discuss the ways technology is changing the learning experience.

The hashtag for the event is #FOTE11 so I made a NodeXL SNA Map -

The following is a map of the connections among the Twitter users who recently tweeted the word #fote11 when queried on October 7, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: fote-conference.com/wordpress/

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: www.flickr.com/photos/marc_smith/6221247656/sizes/l/in/ph…

Top most between users:
@fotie
@jamesclay
@timbuckteeth
@hopkinsdavid
@dajbelshaw
@eingang
@fredgarnett
@pressfuturist
@grahambm
@eyebeams

Graph Metric: Value
Graph Type: Directed
Vertices: 251
Unique Edges: 2904
Edges With Duplicates: 1908
Total Edges: 4812
Self-Loops: 730
Connected Components: 7
Single-Vertex Connected Components: 6
Maximum Vertices in a Connected Component: 245
Maximum Edges in a Connected Component: 4803
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 2.246839
Graph Density: 0.052988048
NodeXL Version: 1.0.1.179

The data set for this network is available from the NodeXLGraphGallery.

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

Connections among the Twitter users who recently tweeted the word Oracle when queried on October 5, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

Top most between users:
@benioff
@oracleopenworld
@salesforce
@arstechnica
@sai
@oracledatabase
@sapjobswithace
@kirbylunger
@capgemini
@reuters_biz

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 2747
Edges With Duplicates: 827
Total Edges: 3574
Self-Loops: 1102
Connected Components: 425
Single-Vertex Connected Components: 396
Maximum Vertices in a Connected Component: 541
Maximum Edges in a Connected Component: 2923
Maximum Geodesic Distance (Diameter): 10
Average Geodesic Distance: 3.475208
Graph Density: 0.00226026
NodeXL Version: 1.0.1.179

A related map represents the connections among people who tweeted the term #OOW11

These are the connections among the Twitter users who recently tweeted the word oow11 when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: www.oracle.com/openworld/live/on-demand/index.html

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:
@oracleopenworld
@oracle
@oracletechnet
@rwang0
@devops_borat
@salesforce
@mysql
@emcoracle
@oraclemix
@oracledatabase

Graph Metric: Value
Graph Type: Directed
Vertices: 670
Unique Edges: 5882
Edges With Duplicates: 1573
Total Edges: 7455
Self-Loops: 881
Connected Components: 45
Single-Vertex Connected Components: 44
Maximum Vertices in a Connected Component: 626
Maximum Edges in a Connected Component: 7405
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.770009
Graph Density: 0.013401602
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

Orange is a major European telecommunications provider that has been focused on what they call the “porous enterprise” – organizations in which many of the previous boundaries and barriers between businesses and customers are gone.  Social media flows in and out of companies and mixes with public collections of discussions about them. Locked down corporate laptops now are joined by employee owned mobile devices.  Consumer social media products now mingle with enterprise social media.  Corporations now engage directly with customers through public consumer social media services.

For example, Orange has several Twitter accounts.  @Orange provides general product information while @Oranger_Conseil and @OrangeHelpers provide customer support for French and English speaking customers, and @orangeapi offers technical information for developers building applications against services offered by Orange.  There are several other related accounts.

The @Orange account has 2,039 followers and is following 621.  It has 673 tweets.  The pattern of connections among these people emerges into a network that can be clustered into sub-groups:


These are the connections among the Twitter users who follow or are followed by Orange when queried on September 30, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users follow one another.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6209515710/sizes/l/in/ph…

Top most between users:
@orange
@orangebusiness
@twitter
@ygourven
@pressecitron
@frenchweb
@lionelfumado
@sosh_fr
@lemondefr
@mashable

Graph Metric: Value
Graph Type: Directed
Vertices: 1587
Unique Edges: 18724
Edges With Duplicates: 26647
Total Edges: 45371
Self-Loops: 0
Connected Components: 229
Single-Vertex Connected Components: 227
Maximum Vertices in a Connected Component: 1358
Maximum Edges in a Connected Component: 45367
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.625415
Graph Density: 0.012709666
NodeXL Version: 1.0.1.179

The dense follows/follower map is a network that represents potential communication, the links indicate that there is a “follows” relationship between any two people.  The network of activated connections created when people reply or mention one another is more sparse.  For example, the @Oranger_Conseil and @OrangeHelpers accounts get mentioned by a number of other users who interact with them.  The connections among these people creates a network pattern:

This is a map of the connections among the Twitter users who recently tweeted the word Orange conseil OR OrangeHelpers when queried on September 30, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

It is characterized by a hub and spoke pattern created as customers who have few connections to one another are linked to one of the hub customer service accounts for English and French speakers.

Top most between users:
@orangehelpers
@orange_conseil
@orange
@thomaslegac
@conorfromorange
@orangeripoff
@presseorange
@orangecomplaint
@lisepressac
@poupimali

Graph Metric: Value
Graph Type: Directed
Vertices: 372
Unique Edges: 474
Edges With Duplicates: 3308
Total Edges: 3782
Self-Loops: 73
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 371
Maximum Edges in a Connected Component: 3781
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 2.356011
Graph Density: 0.008361592
NodeXL Version: 1.0.1.179

Mentions of these accounts are good indications that the topic is the Orange Telecom company and not the many other Orange entities (like Orange County, the fruit, and the color, among others).  Searching for the term “Orange” or even “#Orange will likely bring back a large amount of these “name-space collisions” – overlapping uses of the term “Orange”.

These are the connections among the Twitter users who recently tweeted the word #Orange when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

This network of connections among the population of people who tweeted the term “#Orange has many “isolates” – users who do not follow, reply or mention any other person in the network.  These people all tweeted “#Orange” but they lack any connection to anyone else who did so.

The large cluster of connected users is a group of people discussing the Orange Telecom company, while the other clusters involve people discussing the colors of Autumn (Pumpkins!).

Top most between users:
@bluetouff
@laouffir
@fbrahimi
@presseorange
@damiendouani
@gregfromparis
@eogez
@thomaslegac
@isabellespanu
@challenges

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 2282
Edges With Duplicates: 960
Total Edges: 3242
Self-Loops: 1170
Connected Components: 584
Single-Vertex Connected Components: 515
Maximum Vertices in a Connected Component: 155
Maximum Edges in a Connected Component: 1123
Maximum Geodesic Distance (Diameter): 8
Average Geodesic Distance: 2.787863
Graph Density: 0.001827828
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

At the end of the conference on October 5th, the network of connections among the people who tweeted about Wikisym looked like this:

These are the connections among the Twitter users who recently tweeted the word wikisym when queried on October 5, 2011, scaled by numbers of followers (with outliers thresholded). Connections are created when users reply, mention or follow one another.

See: www.wikisym.org/

Top most between users:
@edchi
@jfelipe
@phauly
@readermeter
@hfordsa
@wikisym
@clifflampe
@staeiou
@geoplace
@wikimedia

Graph Metric: Value
Graph Type: Directed
Vertices: 170
Unique Edges: 1163
Edges With Duplicates: 2165
Total Edges: 3328
Self-Loops: 658
Connected Components: 5
Single-Vertex Connected Components: 3
Maximum Vertices in a Connected Component: 165
Maximum Edges in a Connected Component: 3319
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.385502
Graph Density: 0.052941176
NodeXL Version: 1.0.1.179

In contrast, a few days earlier, these are the connections among the Twitter users who recently tweeted the word wikisym when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

Top most between users:
@wikisym
@jfelipe
@andicat
@geoplace
@staeiou
@dirkriehle
@wikimedia
@edchi
@bkeegan
@hfordsa

Graph Metric: Value
Graph Type: Directed
Vertices: 78
Unique Edges: 459
Edges With Duplicates: 354
Total Edges: 813
Self-Loops: 132
Connected Components: 10
Single-Vertex Connected Components: 8
Maximum Vertices in a Connected Component: 68
Maximum Edges in a Connected Component: 800
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.207506
Graph Density: 0.086913087
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

Marc Smith on Twitter.

The recent Strata Conference in New York City gathered many leaders in big data, data mining, and information visualization.

Link: www.flickr.com/photos/marc_smith/6203313170

Connections among the Twitter users who recently tweeted the word Strata when queried on September 20, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: strataconf.com/summit2011

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: www.flickr.com/photos/marc_smith/6203313170/sizes/l/in/ph…

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:
@timoreilly
@strataconf
@doctorow
@acroll
@ticbeat
@datacenter
@hnshah
@oreillymedia
@markmadsen
@unglobalpulse

Graph Metric: Value
Graph Type: Directed
Vertices: 1118
Unique Edges: 5813
Edges With Duplicates: 6522
Total Edges: 12335
Self-Loops: 1010
Connected Components: 9
Single-Vertex Connected Components: 8
Maximum Vertices in a Connected Component: 1110
Maximum Edges in a Connected Component: 12327
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.575622
Graph Density: 0.00647018
NodeXL Version: 1.0.1.177

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.
The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

This is a map of the connections among the Twitter users who recently tweeted the word HealthTap when queried on September 28, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: healthtap.com/

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: www.flickr.com/photos/marc_smith/6198785341/sizes/l/in/ph…

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:
@lifehacker
@healthtap
@drpam4women
@androidandme
@drpam
@amazonwireless
@jonmrich
@etseng
@mobilehealth
@doctor_v

Graph Metric: Value
Graph Type: Directed
Vertices: 638
Unique Edges: 3630
Edges With Duplicates: 1337
Total Edges: 4967
Self-Loops: 865
Connected Components: 183
Single-Vertex Connected Components: 167
Maximum Vertices in a Connected Component: 426
Maximum Edges in a Connected Component: 4677
Maximum Geodesic Distance (Diameter): 11
Average Geodesic Distance: 3.098025
Graph Density: 0.008715423
NodeXL Version: 1.0.1.177

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.
The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

These slides provide an overview of the goals and accomplishments of the Social Media Research Foundation:

I will speak at Purdue University on September 22 and 23, 2011 about mapping social media networks.

My host is Sorin Matei, professor of Communications, who has been researching the social structure of social media networks.

I will also speak at Professor Matei’s class: COM 63200 On-line Interaction and Facilitation

Here is an example map of the connections among the people who tweeted the word “Purdue” on September 16th, 2011:

Connections among the Twitter users who recently tweeted the word Purdue when queried on September 16, 2011, scaled by numbers of tweets (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: www.purdue.edu/

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

(Edges connecting users are bundled and curved with recent features added to NodeXL v.177.)

A larger version of the image is here: www.flickr.com/photos/marc_smith/6155750905/sizes/o/in/ph…

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:
@lifeatpurdue
@jajuanjohnson
@stfu_gabby
@purdueexponent
@charliienosheen
@mbrister2
@boilerfootball
@purduesports
@hipandresanbo
@cheesebrrrrr

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 4045
Edges With Duplicates: 706
Total Edges: 4751
Self-Loops: 977
Connected Components: 429
Single-Vertex Connected Components: 395
Maximum Vertices in a Connected Component: 528
Maximum Edges in a Connected Component: 4134
Maximum Geodesic Distance (Diameter): 12
Average Geodesic Distance: 3.517707
Graph Density: 0.003507508
NodeXL Version: 1.0.1.177

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

The effect is visually striking.  Here is a before and after with the same network:

Curved Edges, no Edge Bundles

Curved and bundled edges.

In future releases we will expose more controls for configuring the bundles.

NodeXL is extendable.  3rd Party developers have been building data providers that can plug into NodeXL that connect the network visualization tool to sources of network data.  We now have three providers of extensions to NodeXL: VOSON for WWW hyperlink networks, the Exchange Spigot for NodeXL for extracting enterprise email networks, and the Facebook Spigot for NodeXL that extracts your own Facebook network for analysis and visualization!

We welcome additional data provider projects!  Have a network? Connect it to NodeXL with the simple directions listed here.

Import hyperlink networks into NodeXL with the VOSON System – a web-based software incorporating web mining, data visualisation, and traditional empirical social science methods (e.g. social network analysis, SNA).
http://voson.anu.edu.au/node/13#VOSON-NodeXL

http://facebookimporter.codeplex.com/

The NodeXL team from the Social Media Research Foundation has released a web application that allows users to upload network files and download files other users have uploaded.

The Social Media Research Foundation is dedicated to Open Tools, Open Data, and Open Scholarship.

The NodeXL Graph Gallery on the Web enables the research community to collectively gather and share data sets.

Users of NodeXL will notice a new NodeXL>Data>Export>To Web Gallery menu option which opens this dialog box:

Users will be able to browse the NodeXL Web Gallery for graphs that were recently uploaded:

Users will be able to search for graphs that contain certain keywords:

Users can then get details about the network graph and download those files they would like to investigate.

 

Please vote on our SxSW 2012 panel:

Thanks for your vote for our SxSW panel!

Here is a network visualization with all the edges drawn as straight lines:

The same network graph can be drawn with slightly curved edges:

The result is a slight improvement in readability.

To start, the edges curve just a bit.  Soon, we will allow user control over the extent of the curvature.

Turn on curved edges by checking the box in the Graph Options dialog accessed from the graph pane.

A detailed outline of all the topics covered is available:

The help file will be updated as new features are added.

NodeXL Excel Template Help File:

An Overview of Network Graphs
A Quick Tour of the NodeXL Workbook
The NodeXL Worksheets
The Graph Pane
The NodeXL Ribbon Tab
Creating a Simple Graph
Directed vs. Undirected Graphs
Graphs with Isolated Vertices
Zooming, Moving Around and Scaling the Graph
Zooming the Graph
Moving Around the Graph
Scaling the Graph
Selecting Graph Elements
Selecting Vertices
Selecting Edges
Changing How the Graph Looks
Default Visual Properties: Graph Options
Setting Visual Properties for Individual Edges, Vertices or Groups
Automatically Calculating Visual Properties for All Edges, Vertices or Groups
Labeling Edges and Vertices
Changing How the Graph is Laid Out
Layout Algorithms
Selectively Laying Out Parts of the Graph Again
Snapping Vertices to a Grid
Layout Options
Saving an Image of the Graph
Analyzing the Graph
Calculating Graph Metrics
Creating Subgraph Images
Using Dynamic Filters
Working with Options
Using the Current Workbook’s Options for New Workbooks
Exporting and Importing Options
Resetting All Options
Working with Groups
Creating Groups
Creating Groups by Vertex Attribute
Creating Groups by Connected Component
Creating Groups by Cluster
Manually Creating Groups
Understanding the Group Worksheets
How Groups Are Shown in the Graph Pane
Selecting Groups
Collapsing and Expanding Groups
Removing Groups
Showing and Hiding Workbook Columns
Showing and Hiding Graph Elements
Importing Graph Data
Importing Graph Data from Other Programs
Importing Graph Data from Another Workbook
Importing Graph Data from Email
Importing Graph Data from Online Social Networks
Exporting Graph Data
Exporting Graph Data to Other Programs
Exporting Graph Data to Another Workbook
Merging Duplicate Edges
Automating Common Tasks
The NodeXL Network Server
Where to Go for More Information

This is a map of the connections among the people who tweeted the terms “FOCAS11″ or “Aspen Institute” on August 2, 2011.

Connections among the Twitter users who recently tweeted the word Aspeninstitute OR FOCAS11 when queried on August 2, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: www.aspeninstitute.org/policy-work/communications-society…

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: www.flickr.com/photos/marc_smith/6001893675/sizes/o/

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Top most between users:
@aspeninstitute
@lancearmstrong
@emptywheel
@knightfdn
@pkedrosky
@bostonreview
@josefjohann
@kgosztola
@utknightcenter
@sowers

Graph Metric: Value
Graph Type: Directed
Vertices: 324
Unique Edges: 649
Edges With Duplicates: 743
Total Edges: 1392
Self-Loops: 339
Connected Components: 81
Single-Vertex Connected Components: 76
Maximum Vertices in a Connected Component: 240
Maximum Edges in a Connected Component: 1284
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.67739
Graph Density: 0.007023277
NodeXL Version: 1.0.1.173

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

Here is a map of the connections among the people who tweeted the term “AEJMC” on August 7, 2011:

The top most between people in this network are:@aejmc, @jlab, @karenrussell, @terryflynn, @natcomm, @tmccorkindale, @derigansilver, @tkell, @aejmconlineads, and @jeremyhl:

I will present a Workshop on Social Media Network Analysis and NodeXL at the 9 August 2011 – Association for Education in Journalism and Mass Communication (http://www.aejmc.com) in St. Louis, Missouri along with my colleague Professor Hernando Rojas, from the School of Journalism & Mass Communication, University of Wisconsin – Madison.

See: http://www.aejmc.com/home/events/annual-convention/

The Association for Education in Journalism and Mass Communication (AEJMC) is a nonprofit, educational association of journalism and mass communication educators, students and media professionals. The Association’s mission is to promote the highest possible standards for journalism and mass communication education, to cultivate the widest possible range of communication research, to encourage the implementation of a multi-cultural society in the classroom and curriculum, and to defend and maintain freedom of communication in an effort to achieve better professional practice and a better informed public.

Our session is:

Using NodeXL for Social Network Analysis
Tuesday — 2 pm to 5 pm
Presented by Communication Theory and Methodology Division
This pre-conference workshop examines social network analysis. Social network analysis can be used to examine message boards, blogs, and friend networks (amongmany other phenomena). Participants will learn to use the NodeXL program to conduct a network analysis. For information, contact Michel M. Haigh, Pennsylvania State University at mmh25@psu.edu.

A new NodeXL data provider (“spigot“) is now available to extract these social networks from Exchange servers.

This is the Exchange Spigot for NodeXL a configurable interface for defining and executing a query against a Microsoft Exchange email server designed to be used with NodeXL.

NodeXL is an easy to use add-in for the familiar Excel spreadsheet that adds support for network overview, discovery and exploration. The free and open NodeXL project has several import data providers that extract and display networks from popular social media network repositories like Twitter, Flickr, YouTube, the WWW and more.

By adding an Exchange Spigot to your installation of NodeXL you gain a simple way to extract networks from your own email stored on an Exchange server. With appropriate permissions and in compliance with local and legal requirements, the Exchange Spigot can access email and extract social network data across multiple email user accounts.

If available, Active Directory metadata is integrated with email data extracted from the Exchange server. Profile metadata is added to the network workbook when available (typically only for email from other employees, not people from outside your company or division).

The Exchange Spigot for NodeXL is one of several related projects to provide a connection between valuable sources of network data structures and the easy-to-use network browser, NodeXL. Additional projects have implemented connections to web crawlers, Facebook, and soon, SharePoint.

The Exchange Spigot for NodeXL project team includes: Luka Potkonjak, Milos Lazarevic, Ivan Vujic, Natasa Milic-Frayling, and Marc Smith.

The Exchange Spigot for NodeXL project is coordinated by the Social Media Research Foundation (http://www.smrfoundation.org) and receives generous support from Microsoft Research.

NodeXL automatically generates a network legend and displays it when the Graph Elements menu is opened:

And the “Legend” option is selected.

This will place a legend at the bottom of the visualization canvas:

Starting in version .165 of NodeXL we have supported the idea of an options file that can be imported, exported and exchanged among users.

If you have set all the knobs and dials of your copy of NodeXL just right, you can export these adjustments and configurations into a single file.  Use the NodeXL>Options>Export feature to create a named file containing your settings.  You can now exchange this file with others.  If you receive an options file, you can use the NodeXL>Options>Import feature to pick it out from the file system and set your copy of NodeXL to the settings defined in that file.

If you use the related NodeXL>Options>Use Current for New feature you can set the defaults for NodeXL to the settings contained in any imported options file.

I have been fine tuning a settings file that generates network visualizations that are tailored for the Twitter networks I have been making many of.  I have saved these sample settings file for NodeXL and you can try them yourself here: NodeXL-Twitter Network-Options Settings.NodeXLOptions.

Download this file, rename it by removing the .txt extension and load it into your copy of NodeXL.

I am very interested in the Quantified Self conference to be held in Mountain View, California, May 28 and 29.  While I have attended just a few of the in-person meet-ups, which were engaging and intriguing events, I have followed the blog and tweet stream closely.  These events feature short presentations about practices, prototypes, and products that record information about our own behavior and activity.  It is great to hear that, as the meet-ups grew to become very large, a conference has been organized to accommodate the demand and growing interest in the intersection of sensors and other devices with medical and personal self-monitoring.  Using a variety of devices, our lives can now create detailed inscriptions that illuminate our behavior and patterns with novel clarity and detail. (See: http://quantifiedself.com/conference/)

I plan to attend.  I will speak on Sunday morning at a 10:30 breakout session on using social media network analysis to map personal and collective social media spaces.

Quantifying the “Quantified Self” discussion in Twitter: Here is a map of the connections among the people who recently tweeted the string “quantified self” on May 25th, 2011.

Click for larger image.

The top most between participants in this graph were:

@timoreilly
@quantifiedself (-1)
@harscoat (-)
@edyson
@qsparis
@egadenne (-4)
@agaricus (-)
@brunoaziza
@adriana872
@chloester

Bolded users also appeared in a previous map made earlier in the year, on January 20, 2011, which looked like this:

This is the list of the most “between” users in this network on January 20th, 2011:

The most between participants in this graph are: @quantifiedself, @egadenne, @harscoat, @genomera, @neufit, @jxa, @agaricus, @emergentorder, @bulletproofexec, @2healthguru.

The topics discussed in the quantifiedself tweet stream can be rendered as a network graph based on words that co-occur:

The ACM CHI 2011 Conference is taking place in Vancouver, BC.

May 10, 2011

May 8, 2011

These are the connections among the Twitter users who recently tweeted the word chi2011 when queried on May 8, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

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 network map is here: www.flickr.com/photos/marc_smith/5701022497/sizes/o/in/ph…

Top most between users:
@chi2011
@msftresearch
@edchi
@jeffbigham
@landay
@evanmpeck
@acagamic
@clifflampe
@yardi
@eytanadar

Graph Metric: Value
Graph Type: Directed
Vertices: 421
Unique Edges: 3849
Edges With Duplicates: 324
Total Edges: 4173
Self-Loops: 0
Connected Components: 32
Single-Vertex Connected Components: 29
Maximum Vertices in a Connected Component: 384
Maximum Edges in a Connected Component: 4146
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.553249
Graph Density: 0.022587943

NodeXL Version1.0.1.167

Data set in NodeXL format.

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

• Group Frames: If your graph has groups and you choose to lay out the groups in their own boxes (NodeXL, Graph, Layout, Layout Options), you can now specify the width of the box outlines.

• Constant Edges: When you select an edge, its width no longer changes. NodeXL used to use the same width for all selected edges, even if the edges had varying widths when unselected.
• Group and Vertex Display Harmony:
  • When a graph has groups, you now have more control over how the groups are shown. Go to NodeXL, Analysis, Groups, Group Options.
  • The NodeXL, Show/Hide, Graph Elements, Groups menu item has been replaced with a checkbox in the Group Options dialog box.

  

• Right-Click Group Controls: Menu items for selecting, expanding, collapsing and removing groups are now available in the menu that appears when you right-click the graph pane. (These are just shortcuts for the same menu items that are available in the Ribbon at NodeXL, Analysis, Groups.)

• WYSIWYCC: What You See Is What You Can Click -
  • Hidden edges and vertices (those that have their Visibility cells set to Hide) can no longer be selected in the graph pane.
  • Edges and vertices that have been filtered (NodeXL, Analysis, Dynamic Filters) can no longer be selected in the graph pane.
• Bigger Twitter Lists: When importing a Twitter list network (NodeXL, Import, From Twitter List Network), you can now enter up to 10,000 usernames. The maximum used to be 500.
• UCINET / Matrix Compatibility: Bug fix: When exporting the graph to a UCINET file (NodeXL, Data, Export, To UCINET Full Matrix DL File), isolated vertices didn’t get exported. When exporting the graph to a new matrix workbook (NodeXL, Data, Export, To New Matrix Workbook), isolated vertices didn’t get exported, when importing a graph from a matrix workbook (NodeXL, Data, Import, From Open Matrix Workbook), isolated vertices didn’t get imported. Now they do!

This is a map of the connections among the Twitter users who recently tweeted the words #rw2011 when queried on April 28, 2011, scaled by numbers of followers (with outliers thresholded), connected by replying, mentioning and following.

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 network map is here: www.flickr.com/photos/marc_smith/5666637994/sizes/o/

A detailed list of top most between users is here: www.flickr.com/photos/marc_smith/5666069249/

Top most between users: @nytimes, @skynews, @peoplemag, @royalweddingcnn, @itv_news, @katewilliam2011, @julieetchitv, @abc, @cbsnews, @abcroyals

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 1735
Edges With Duplicates: 531
Total Edges: 2266
Self-Loops: 0
Connected Components: 271
Single-Vertex Connected Components: 246
Maximum Vertices in a Connected Component: 701
Maximum Edges in a Connected Component: 2199
Maximum Geodesic Distance (Diameter): 11
Average Geodesic Distance: 3.892905
Graph Density: 0.001984985
NodeXL Version: 1.0.1.166

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

I will be speaking at the Oxford Internet Institute on July 13 and 14, 2011 as part of the Summer Doctoral Program.

I will speak about the use of social network analysis and visualization techniques to analyze and understand social media.

The Summer Doctoral Program is designed to

…bring together advanced doctoral students engaged in dissertation research relating to the Internet and other ICTs. By sharing their work and learning from leading academics in the field, students can enhance the quality and significance of their thesis research and create a peer network of excellent young researchers.

The 2011 Summer Doctoral Programme will draw substantially upon the OII’s research strengths and will involve participation from many of our faculty, together with colleagues from other partner institutions. It will emphasise methodological innovation and good practice in research design, and will expose students to the benefits of discussing their research in a multi-disciplinary teaching environment.”