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):

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.

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.

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/

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.

Summer Social Webshop
on
Technology-Mediated Social Participation
University of Maryland, College Park
August 23-26, 2011

Eventful.  The 2011 Webshop at the University of Maryland was certainly that with both an earthquake and a hurricane to mark the start and end of the event.  We really moved heaven and earth at this workshop.

In 4  days, 20 talks, 45 students, an earthquake, a hurricane and many new connections – the Webshop touched on a set of related concepts, methods, and findings about ways to use communication and computation technology to help groups, neighborhoods, cities, states, and nations work collectively towards common goals.

Several years ago a program at the University of Maryland called “Webshop” (Web Workshop) was organized by Professor John Robinson and held for three consecutive Summers.  I visited and spoke at two of these events and know many people who attended or spoke at one or more and remember the event enthusiastically.   The students who attended include some of the now leading researchers in the field of social science studies of the internet.  There is an impressive alumni list.

The last Webshop was held in 2003 and many years and significant changes have occurred in the time since. Twitter, Facebook, StreetView, iPad,FourSquare, Android, Kinect, EC2, Mechanical Turk, Arduino, were all new or non-existent when the first Webshops were run.  Today we have more reason than ever to focus on the details and patterns of computer-mediated human association. Ever more people channel more of their communications with others through more digital media, often of the social kind.  A new data resource for the social sciences is growing in scale and promise: from billions of events it is possible to start to build a picture of an aggregate whole, and to start to grasp the terrain and landscape of social media.

After many years of inactivity, the Summer Social Webshop (@Webshop2011) happened again!  With the generous support of the National Science Foundation and additional assistance from Google Research, on August 23-26, 2011 at the University of Maryland, College Park, a group of students heard and engaged with more than two dozen leading researchers exploring digital social landscapes from a variety of perspectives.  Organized by a collaboration between the University of Maryland’s Human Computer Interaction Laboratory (HCIL), the College of Information Studies, the Sociology and Computer Science Department, and the Social Media Research Foundation, the event gathered students from a wide range of disciplines to get a concentrated dose of advanced efforts to gather data from social media and people’s understanding and practices around digital technologies.   Doctoral students in computer science, iSchools, sociology, communications, political science, anthropology, psychology, journalism, and related disciplines applied to attend the 4-day intensive workshop on Technology-Mediated Social Participation (TMSP).  The workshop explored the many ways social media can be applied to national priorities such as health, energy, education, disaster response, political participation, environmental protection, business innovation, or community safety.  The workshop attracted graduate students at US universities studying social-networking tools, blogs and microblogs, user-generated content sites, discussion groups, problem reporting, recommendation systems, mobile and location aware media creation, and other social media.

-

Organizers

Alan Neustadtl (@smilex3md) - Sociology, University of Maryland
Jennifer Preece (@jenpre) – iSchool, University of Maryland
Marc Smith(@Marc_Smith) - Social Media Research Foundation
Ben Shneiderman (@benbendc) - Computer Science, University of Maryland
PJ Rey (@pjrey) - Sociology, University of Maryland, Student Coordinator

-

Photos

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

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.

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

Summer Social Webshop
on
Technology-Mediated Social Participation
University of Maryland, College Park
August 23-26, 2011

Several years ago a program at the University of Maryland called “Webshop” (Web Workshop) was organized by Professor John Robinson and held for three consecutive Summers.  I visited and spoke at two of these events and know many people who attended or spoke at one or more and remember the event enthusiastically.   The students who attended include some of the now leading researchers in the field of social science studies of the internet.  There is an impressive alumni list.

The last Webshop was held in 2003 and many years and significant changes have occurred in the time since.  Twitter, Facebook, StreetView, iPad, FourSquare, Android, Kinect, EC2, Mechanical Turk, Arduino, were all new or non-existent when the first Webshops were run.  Today we have more reason than ever to focus on the details and patterns of computer-mediated human association. Ever more people channel more of their communications with others through more digital media, often of the social kind.  A new data resource for the social sciences is growing in scale and promise: from billions of events it is possible to start to build a picture of an aggregate whole, and to start to grasp the terrain and landscape of social media.

The Summer Social Webshop (@Webshop2011) is happening again!  With the generous support of the National Science Foundation and additional assistance from Google Research, this August 23-26 at the University of Maryland, College Park, a group of students will hear and engage with more than two dozen leading researchers exploring digital social landscapes from a variety of perspectives.  Organized by a collaboration between the University of Maryland’s Human Computer Interaction Laboratory (HCIL), the College of Information Studies, the Sociology and Computer Science Department, and the Social Media Research Foundation, the event will gather students from a wide range of disciplines to get a concentrated dose of advanced efforts to gather data from social media and people’s understanding and practices around digital technologies.   Doctoral students in computer science, iSchools, sociology, communications, political science, anthropology, psychology, journalism, and related disciplines are invited to apply to attend this summer’s 4-day intensive workshop on Technology-Mediated Social Participation (TMSP).  The workshop explores the many ways social media can be applied to national priorities such as health, energy, education, disaster response, political participation, environmental protection, business innovation, or community safety.  The workshop should be of interest to graduate students at US universities studying social-networking tools, blogs and microblogs, user-generated content sites, discussion groups, problem reporting, recommendation systems, mobile and location aware media creation, and other social media.

For more information, please contact Alan Neustadtl (alan.neustadtl@gmail.com).

-

Photos

I will be speaking at the Jacob T. Schwartz International School for Scientific Research week long Lipari School on Computational Social Science , July 17 – July 23, 2011, Lipari Island, Italy.

This year’s program is dedicated to Computational Social Science: Text and Decisions

Speakers:

• Claudio Cioffi-Revilla: Director of the Center for Social Complexity, Krasnow Institute for Advanced Study, George Mason University, Washington DC.
• Huan Liu: Community Detection and Mining in Social Media [abstract]
  School of Computing, Informatics, and Decision Systems Engineering, Arizona State University
• Roel Popping: Computer-assisted text analysis, and the relevance of decision making and text mining [abstract]
  Department of Sociology, University of Groningen

Tutorials

• Marc A. Smith: Charting Collections of Connections in Social Media: Maps and Measures with NodeXL [abstract]
  Chief Social Scientist, Connected Action Consulting Group
• Calogero Zarba: Introduction to matrix algebra [abstract]
  Neodata Intelligence s.r.l., Italy
• Alessandro Pluchino: Netlogo: An agent based simulation programmable environment [abstract], University of Catania, Italy

http://www.ischools.org/iConference11/2011index/

Our paper, Finding Social Roles in Wikipedia,  about the variety of  roles people perform in Wikis received the best paper award (along with 4 others) in a field of 86 papers.   The 2011 iConference accepted 86 papers, and had about 550 attendees.

The paper is authored by: Howard T. Welser at Ohio University, Austin Lin at Cornell University and Microsoft, Dan Cosley, Fedor Dokshin, Gueorgi Kossinets and Geri Gay at  Cornell University, and Marc Smith from Connected Action.

The paper pdf pre-print is available here: http://oak.cats.ohiou.edu/~welser/Welser.Cosley.plus.Wiki.Roles.pdf

The link to the ACM abstract and pdf: http://portal.acm.org/citation.cfm?id=1940778&CFID=9933318&CFTOKEN=58981138

Abstract: This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential “structural signatures” that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month’s cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles.

Web Science Doctoral Summer School 2011

I will be attending and speaking at the upcoming 2011 Web Science Trust Graduate Summer School tobe held at DERI, NUI Galway Ireland from July 6th to July 13th, 2011.  I will speak about social media network analysis and run a workshop on using NodeXL.  There is a packed schedule featuring a great group of researchers who are gathering at the event, including:

* Prof. Wendy Hall is Professor of Computer Science at the University of Southampton, UK, and Dean of the Faculty of Physical and Applied Sciences.
* Prof. Nigel Shadbolt is Professor of Artificial Intelligence (AI) and Deputy Head (Research) of the School of Electronics and Computer Science at the University of Southampton.
* Prof. Stefan Decker is a professor at the National University of Ireland, Galway, and director of the Digital Enterprise Research Institute.
* Dr. Harith Alani is a senior lecturer at the Knowledge Media Institute, where he is heading a group specialising in Social Semantics.
* Dr. Bernie Hogan is a Research Fellow at the Oxford Internet Institute. He specializes in novel methods for online data capture and analysis, especially via social media.
* Prof. Scott Kirkpatrick has a background in physics (AB Princeton, PhD Harvard) and 15 years of developing new technologies at IBM’s TJ Watson Research Center before coming to the Hebrew University in 2000.
* Prof. Enric Plaza holds a Ph.D. in Computer Science by the Technical University of Catalonia (UPC) and is Research Professor of the Spanish Council for Scientific Research (CSIC).
* Prof. Steffen Staab is the director of Institute WeST – Web Science and Technologies and of the Institute for Computer Science of the University of Koblenz-Landau, Germany.
* Dr. Derek Greene is a Research Fellow at the School of Computer Science and Informatics, University College Dublin. As part of the Clique Research Cluster, his focus is on network analysis and community finding, with a particular emphasis on dynamic mobile and social media networks.
* Dr. Michael Hausenblas is a Research Fellow at DERI, NUI Galway, where he leads the Linked Data Research Centre.
* Dr. Daniele Quercia is Horizon researcher at the University of Cambridge (UK). he is interested in computational social science, web science, and social computing.
* Dr. Markus Strohmaier is an Assistant Professor at the Knowledge Management Institute, Faculty of Computer Science at Graz University of Technology.

The event is focused on the methods and theories needed to grasp the nature of the social implications of the rapid changes in the technologies of communication and computation:

“The Web is the largest technological artefact in existence, comprising a global network of information sites and services. It is a social machine, delivering information between people and communities, embedded in almost all processes of human society: education, medicine, science and technology, commerce, entertainment and social activity. It is often simply supposed that the Web is a neutral technology, a stable computing platform for the delivery of information and services. What is overlooked is that the Web is changing constantly in response to the demands of human society. Incremental innovations leads to changes in how people use the Web and in turn how Web technology responds to changed human interaction. Small technological decisions influence how individuals use the Web and ripple out to have unanticipated macro-effects. Sometimes these effects are beneficial such as the emergence of Web 2.0 technologies. Other technologies such as the rise of spam bots or ‘blackhat’ search engine optimisation techniques clutter the Web with irrelevant, distracting information. While influential corporations such as Google, Facebook and Microsoft generate huge revenues from the Web, the Web itself is owned by everyone and no-one. We need to fully understand the demands placed on the Web by human society, so that its fate does not lead to a ‘tragedy of the commons’ but to a sustainable technological resource for the future.”

http://webscience.deri.ie/index.html

Read More

Lists are a recent feature of Twitter which enable users to compile collections of users to follow in a single tweet stream.  People can add up to 500 people to follow on a single list.    People on a list may be connected to one another if one follows the other.

Some people on a list may have many connections.  Some have only a few or even no connections to others on the list.

In version 161 of NodeXL you may now create maps of the connections among a list of Twitter users.

There are two options in this feature.  One makes use of the List functions in Twitter.  If you request the map for a single twitter list, NodeXL will build a map of the connections among all the people Twitter reports as being on the list.  You can create and manage the people on a list using the Twitter list features, or select an existing Twitter list created by other users.

A second option in this feature accepts a list of up to 10,000 twitter user names pasted into the query text box.  If you have a list of users and want a map of how they are connected, and the list is not already in Twitter, just paste them here and get a map.

Either way, a connection will be created for every two users if one follows the other.

Here is an example of the network map of the Twitter list of social network analysis people maintained by Valdis Krebs: valdiskrebs\network-analysts

I will be speaking at the next Predictive Analytics World in San Francisco– March 14-15, 2011 about measuring and mapping social media networks with NodeXL:

Social Data: Advanced Methods – Social (Media) Network Analysis with NodeXL
www.predictiveanalyticsworld.com/sanfrancisco/2011/agenda.php#day2-13a

Networks are the common data structure that unify the otherwise diverse range of social media services. In this session learn how to extract social networks from various social media systems and analyze and visualize the structures found in collections of connections. Learn to use the free and open NodeXL add-in for Excel 2007/2010 to analyze email, twitter, facebook, youtube, www, flickr, and wiki networks.

Here is an example – a map of the connections among the people who recently tweeted the string “#pawcon”.

Connections among the Twitter users who recently mentioned PAWCON when queried on March 14, 2011 including tweets from the period 3/10/2011 15:13 to 3/15/2011 3:51, scaled by numbers of followers.
NodeXL is available from www.codeplex.com/nodexl

The top most between contributors were: @tapan_patel, @jamet123, @zementis, @pawcon, @ibmspss, @sasanalytics, @kristinevick, @waynettetubbs, @dataspora, @inside_r

Once you start creating and collecting network graphs you may find you can build a significant collection: hundreds, thousands or tens of thousands of graphs may result from a study or on-going monitoring project. In a series of features in the NodeXL project we have enabled a workflow for constructing many social media  network graphs using the Network Server component (see: How to schedule the creation of a network with NodeXL and Windows Task Scheduler and: New NodeXL Network Server (v1.0.1.126) – Frequently Asked Questions).  This can result in a collection of *many* NodeXL (and GraphML) network files.  Then we implemented features that enabled “Automation”, the application of many operations in NodeXL (metrics calculation, autofill columns, layout and more) to many files without direct human engagement (see: Automatic for the people (who use the latest NodeXL!). Release v.1.0.1.128 and: Fully automatic: NodeXL can build your network graphs hands free).

A single workbook may contain data from a single NodeXL data collection, run on a particular day and collecting data from a few hours or days back from that moment  (depending on factors like the volume of activity around the selected keyword and the depth of the twitter search catalog, which is often not more than a week or two long and much shorter for active topics).  An example of a single network slice is this recent map of the connections among people who mentioned “microsoft research” in Twitter on a single day (December 18th, 2010):

:

This is a single slice of the network, a day out of months of activity.  A still frame can tell a rich story: this is a picture of a crowd that has gathered to discuss a topic of common interest: “microsoft research“.  It illustrates a structure common to many large discussions of popular topics — a large set of isolates (the rows at the bottom) who were not observed to have a followed, mentions, or replies relationship to anyone else who tweeted the same term.  These are casual mentioners of the topic.  At the end of these rows are a small number of dyads, triads, and small components of a handful of people who link to one another but not to the largest connected component. These are pairs or small groups discussing the topic among themselves, but none are connected to a larger component.  Above these rows is the “giant component” — the blob of people who do have a connection to someone else who also tweeted a message containing the same term who in turn have a connection that leads to a large number of others.  The giant component is itself composed of several sub-components of densely connected groups.  At the center of each component are the core users, the people who often hold their cluster together. Between these clusters are the bridges, the people who link otherwise disconnected sub-groups.  At the edges are the peripheral people who have just taken the first step up from being an isolate and have formed a single reply, mention, or follows relationship to someone else who also tweeted the search keyword and can bridge them back to the core of the giant component.  This is a large and active network with hybrid qualities.  There is a “brand” or broadcast element in it: the yellow cluster is a hub and spoke structure centered on the Microsoft Research Twitter account.  These people re-tweet what this account publishes but do not connect to one another.  Just a few of these people set off second and third waves of retweets.  Elsewhere in the graph there are other network structures present, for example the green and blue clusters feature people are centered around their own discussions of the term “microsoft research“.

If you collect many still frames of slices of network activity there is great value in exploring the way the network graph changes over time.  In the most recent release NodeXL provides the first step in a series of features related to time and graph comparison.  You can now create a workbook that aggregates the overall metrics (edge counts, vertex counts, connected component counts, etc.) for a folder full of NodeXL workbooks. In NodeXL follow the menu path: NodeXL>Analysis>Graph Metrics>Aggregate Overall Metrics to get this:

The result of this feature is a workbook with a row containing the summary data from each of the workbooks in the target folder.  Any arbitrary collection of network workbooks can be aggregated but this is particularly useful when the workbooks are sequential time slices.

An example is the time series plot below tracking the rise and fall of several Twitter volume and network measures for the “microsoft research” search term over several months:

This chart tracks the number of vertices (each vertex in this case is a person  our data collector saw tweet about the search term “microsoft research“) in each (almost) daily network snapshot.  In addition the unique edges or connections between these Twitter users are plotted along with the number of people with no connections (“Single-Vertex Connected Components”).  The size of the largest component in the network (“Maximum vertices in a connected component “) is a measure of the changing size of the core community of discussion participants.  Measures like the maximum and average “geodesic” distance provide a rough measure of how long and thin (high values) or generally spherical (low values) a particular network is shaped. A “geodesic” is the longest path that can be walked through the network.  Long skinny networks may indicate the presence of loosely connected smaller groups that have a few people who act as bridges.  Low geodesic values suggest dense networks with people connected to many others with few isolates and sub-groups.

The peaks are closely associated with major events on the Microsoft Research calendar, like the 2010 Microsoft Research Faculty Summit event I attended in early July.

I find the ratios between measures of the size of the large network component and the population of isolates to be interesting.  As events go on over a period of days more people connect with others who are talking about the same topic, growing the size of the large connected component.  But often the isolate population also grows during this time as people at the periphery of the topic network catch sight of mentions of the event and tweet about it.  I could imagine one goal of social media management to be the conversion of isolates to connected component members.  Those who follow, reply or mention even a single other person also talking about a topic are more likely to return and engage than those who have zero connections.  It is not clear if more connections provide a linear increase in continued engagement, I suspect that the main effect is at the zero/one divide and drops off in effect after the first dozen or so connections.  Encouraging cohesion and network density by replying to isolates and encouraging others to do so may help keep a social media population focused and growing.

This feature follows the work done in the ManyNets project (http://www.cs.umd.edu/hcil/manynets/) at the University of Maryland by Manuel Freire, Catherine Plaisant, Ben Shneiderman, Awalin Sopan, and Miguel Rios.  ManyNets also created a framework for managing the metadata about collections of networks. ManyNets provides for  much richer interactions and linkages to the underlying networks than NodeXL can do so far.

In this map nodes represent the major feature groups and functions in the NodeXL application.

This map will become the default file that will open when you run NodeXL for the first time.  You will see a dialog like this:

Select Yes to have the graph above imported into the workbook.  You can then display the graph using the Show Graph button in the NodeXL menu ribbon.

After that, it will be available via the help menus. When you import the file, all of the data is also available in the spreadsheet part of NodeXL so that you can experiment with changing values there to see the impact in the graph display after you hit the “refresh graphs” button.

Over the coming weeks we plan to release additional sample network data sets that illustrate key concepts and methods in network science.  Suggestions for sample networks are welcome!

I spoke at the Social Tech 2010 conference in San Jose on October 26, 2010.

The event focused on social media technologies and practices in the business to business space.

I spoke on a panel about the application of social network analysis to social media to illustrate the ways SNA can illuminate the shape and key contributors to a social media crowd inside the firewall or on the public Internet.

Other notable speakers included: Thomas Lento from Facebook, Lawrence Liu from Cisco and Michael Wu from Lithium.  The panel was moderated by Tatyana Kanzaveli.

An example of applying social network analysis and visualization is this map of the connections among the people who recently tweeted “socialtech” on the 25th of September, 2010.

The top most between users in this map can be sorted in the spreadsheet, bringing the user accounts for @mprofsevents, @socialtech, @mikewise07, @jowyang, @marketingprofs, @meganleap, @socialmediaclub, @cotweet, @zuberance, @spiral16.

I am coming to understand what is interesting in any particular network visualization by looking at many network visualizations.

Using NodeXL, I have made several maps of  social media networks of people talking about several topics of interest from current events to conferences I attend.  You can find a collection of them on flickr.

I look at these images and look for differences in the number of big clusters: some images have a “double yolk” – that I propose is a necessary (but not necessarily sufficient) condition of defining a topic to be “controversial”.  These two cluster networks have two well defined populations who lack much if any connection across the divide to the “other” side.

Some networks are highly populated but sparse, these are often the networks that form around brands where a central account tweets and is retweeted by many.  But these many lack much connection to one another.  So these brands form broadcast networks, not communities.

Some networks are dense single clusters with few if any “isolates”.  Isolates are people who say a term, and thus appear in the graph, but have no connections (follows, replies, ore mentions) to anyone else in the graph (at least as observed and reported by twitter at that time).  These dense clusters without isolates are topics where everyone is in-group.  Examples, like “scrm”, are technical and business terms that identify medium sized populations with high levels of density.

Have a look and see what patterns you can find.