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):

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.

Ines Mergel is my host.  Prof. Mergel is Assistant Professor of Public Administration, Department of Public Administration and International Affairs, and a Senior Research Associate at the Center for Technology and Information Policy at the Maxwell School of Citizenship and Public Affairs, Syracuse University, NY.

I will speak about the patterns we are finding in the data collected and analyzed by NodeXL.

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/2011/10/22/november-3-2011-seoul-south-korea-international-symposium-on-convergence-technology-contech-2011/?utm_source=rss&utm_medium=rss&utm_campaign=november-3-2011-seoul-south-korea-international-symposium-on-convergence-technology-contech-2011 http://www.connectedaction.net/2011/10/22/november-3-2011-seoul-south-korea-international-symposium-on-convergence-technology-contech-2011/#comments Sat, 22 Oct 2011 19:00:42 +0000 Marc Smith http://www.connectedaction.net/?p=4819

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

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.

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/

This year the IEEE Social Computing conference is being held in Boston, October 9-11, 2011.

The NodeXL team from the Social Media Research Foundation have a paper on our newest layout feature in NodeXL: Group-in-a-box.

Abstract: Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a metalayout for clustered graphs that enables multi-faceted analysis of networks. It uses the treemap space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.

The paper is authored by:

Eduarda Mendes Rodrigues*, Natasa Milic-Frayling†, Marc Smith‡, Ben Shneiderman§, Derek Hansen¶
* Dept. of Informatics Engineering, Faculty of Engineering, University of Porto, Portugal – eduardamr @ acm.org
† Microsoft Research, Cambridge, UK -natasamf @ microsoft.com
‡ Connected Action Consulting Group, Belmont, California, USA – marc @ connectedaction.net
§ Dept. of Computer Science & Human-Computer Interaction Lab, University of Maryland, College Park, Maryland, USA – ben @ cs.umd.edu
¶ College of Information Studies, University of Maryland, College Park, Maryland – dlhansen @ umd.edu

A map of the connections among the people who recently tweeted #SocialCom2011:

Connections among the Twitter users who recently tweeted the word #socialcom2011 when queried on October 10, 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 image is here: www.flickr.com/photos/marc_smith/6232130442/sizes/l/in/ph…

Top most between users:
@danielequercia
@gadgetman4u
@bkeegan
@shaunlawson
@maryheston
@mmiiina
@ronaldomenezes
@theshadowhost
@fergal_reid
@cosleydr

Graph Metric: Value
Graph Type: Directed
Vertices: 36
Unique Edges: 119
Edges With Duplicates: 155
Total Edges: 274
Self-Loops: 105
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 35
Maximum Edges in a Connected Component: 273
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.174551
Graph Density: 0.107936508
NodeXL Version: 1.0.1.179

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/

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/

The Workshop on Information in Networks 2011 was held at NYU September 30-October 1st.

Over the course of the event a number of people tweeted using the hashtag #win2011.

These are the connections among the Twitter users who recently tweeted the word win2011 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: winworkshop.net/

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:http://www.flickr.com/photos/marc_smith/6209064926/sizes/l/in/photostream/

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:

@sinanaral
@winworkshop
@barrywellman
@ladamic
@seanjtaylor
@drewconway
@chrisdiehl
@dylanwalker
@marc_smith
@ariegoldshlager

Graph Metric: Value
Graph Type: Directed
Vertices: 38
Unique Edges: 227
Edges With Duplicates: 193
Total Edges: 420
Self-Loops: 68
Connected Components: 1
Single-Vertex Connected Components: 0
Maximum Vertices in a Connected Component: 38
Maximum Edges in a Connected Component: 420
Maximum Geodesic Distance (Diameter): 3
Average Geodesic Distance: 1.836565
Graph Density: 0.194167852
NodeXL Version: 1.0.1.179

These are the connections among the Twitter users who tweeted the word win2011 when queried a few days earlier on September 30, 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/6198785387/sizes/l/in/ph…

Top most between users:
@sinanaral
@winworkshop
@ariegoldshlager
@drewconway
@chrisdiehl
@kateaanderson
@dylanwalker
@barrywellman
@kristinalerman
@pboothe

Graph Metric: Value
Graph Type: Directed
Vertices: 16
Unique Edges: 60
Edges With Duplicates: 45
Total Edges: 105
Self-Loops: 26
Connected Components: 1
Single-Vertex Connected Components: 0
Maximum Vertices in a Connected Component: 16
Maximum Edges in a Connected Component: 105
Maximum Geodesic Distance (Diameter): 3
Average Geodesic Distance: 1.617188
Graph Density: 0.270833333
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.

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.

I have enjoyed and been educated by my previous visits to South Africa and the Mobile Web in Africa conference, now in its 3rd year.

MWA gathers the many parts of the picture of mobile technologies in play across the African continent.

I will speak again at this year’s event, focusing on social media networks.  My partner is Walter Pike, a marketing professional with a specialty in digital media.

Here is the agenda for the workshop on Friday, November 25th.

Photos from the prior two years of Mobile Web Africa after the jump:

2010

2009

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.

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.

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

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.

On June 22nd in New York City Wired Magazine and the Economist hosted a day of discussions and displays related to the theme of “Nextwork” hosted by Juniper Networks and located at the Tribeca Rooftop.

The Social Media Research Foundation displayed and printed  Nextwork Networks maps of the connections among the people who tweeted about the conference, the speakers, and their companies.

Visit the NextWork Network booth hosted by the Social Media Research Foundation to see a map of the connections among people who tweeted the term “NextWork“.  Networks are important data structures but the tools to access, analyze and visualize networks have been hard to use.  New tools now make networks as easy to handle as making a pie chart.  NodeXL, the free and open network overview, discovery and exploration add-in for the familiar Excel spreadsheet, makes creating network diagrams and gaining insights much simpler (see: http://nodexl.codeplex.com).  Using NodeXL, we have been collecting all the tweets that mention the NextWork event and building a network map of the ways those people connect to one another.  Not everyone is equally connected: some people occupy more strategic locations in the web of connections than others.   Find out who: request your own NodeXL social media network map for your Twitter account or the name of your company or brand (or your competitors) by dropping by the booth (or visit: http://www.smrfoundation.org/nextwork2011).

Here is an example, a map of the connections among the people who recently tweeted the name “Kevin Kelly”, one of the speakers at the event:

This is a map of the connections among the people who tweeted the term “Nextwork”:

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.

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.”

NodeXL allows users to gather vertices into named collections called “Groups”.  This is handy whenever the entities in the network are made up of different types or an algorithm has divided the network into sub-regions based on how densely some vertices connect to one another.  The Groups menu is found in the NodeXL>Analysis menu:

Since version v.132 of NodeXL it has been possible to Collapse a group of vertices (See: Expand and Collapse Groups of Vertices with NodeXL v.132).  When a group is collapsed all of the vertices within that group are removed from the network graph and replaced with a single vertex with a size proportionate to the number of vertices in the group.  A small “+” plus sign indicates that the vertex is a placeholder for a group of vertices.

If the user expands a collapsed group all of the vertices that had been hidden return to positions in the network visualization.  The Groups menu has commands for creating, collapsing, and expanding groups.

NodeXL (v.166) now has the ability to automatically collapse or expand any group of vertices conditionally based on any attribute in the workbook using the Autofill Columns feature.

The NodeXL Autofill columns feature allows users to map data elements to display elements.  At the bottom of this list (you may need to scroll down to see it) you will now find a new row: Group Collapsed?

There are several network metric attributes for each group that are created when the Find Groups and then the Graph Metrics command has been run on a network in NodeXL:

Selecting one of the data items in the drop down allows you to automatically decide if a group with those attributes will be presented in a collapsed or (default) expanded state.  The data about each group include the number of vertices within the group, the number of connections between those vertices, the number of non-unique connections, the number of unique connections among the vertices, the number of self-connections, the number of unique connected components, the number of isolated vertices, the number of vertices in the largest component, the number of edges in the largest component, the maximum and average width of the largest component, and the density of the group.

These metrics allow for the automated processing of the graph to measure each group and apply a test to decide if a group is too dense or populous to be seen in an expanded state.

I will be speaking at the Jacob T. Schwartz International School for Scientific Research week long