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.

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

And the “Legend” option is selected.

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

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.

Recently, we’ve been setting out to set up a new way to set the option settings in NodeXL.  Now we have added a new feature to NodeXL (v.166): savable, selectable configuration option settings files.

This may seem dull but this feature may have a big impact on the ease of use of NodeXL.  This may let the most experienced users of NodeXL share their best practices with the rest of the user community.

Throughout NodeXL you can set a wide range of values, options, and settings.  Change the default size of a font in the Graph Options and we record that setting.  Map the size of a vertex to a value associated with it in Autofill columns  and we record the setting.

Until now, we recorded the settings in several places: some settings went with each workbook, some settings were associated with each machine and stayed on each desktop.  The result was that I could create a great network visualization, save and send someone the workbook and they often would not see what I saw when they opened and visualized the network graph. Why?  People set their settings in lots of different ways, on different machines, creating potentially vastly different results.

Now, NodeXL will record everything about how a network workbook should be rendered in that workbook.

This now also means that expert users can save their settings for NodeXL and exchange them with other users.

NodeXL now has three places to put settings that should make it more reliable to share a workbook and get the same results on different systems.  There will now be a default NodeXL settings file, a per NodeXL workbook settings file (stored in a hidden worksheet in the workbook) and any number of saved settings files.  Users can save their settings in each workbook or decide to save the workbook’s custom set of settings to overwrite the default settings file that will be applied to all subsequent NodeXL workbooks.  Users can also save their settings to one or more named files, which can be shared with others.  Users can import any of these settings files and apply them to an open workbook by selecting NodeXL>Options>Import.

• Each NodeXL workbook now has its own set of options. The options are stored right in the workbook, so if you send a workbook to someone else, she’ll be using the same set of options that you did. (“Options” are the selections you make in NodeXL’s dialog boxes, in the NodeXL tab in the Excel Ribbon, and in the toolbar at the top of the graph pane.)
• If you like the options you’ve selected in a workbook and you want those options to be used for all new NodeXL workbooks, use NodeXL, Options, Use Current for New in the Ribbon.
• You can export a workbook’s options to a separate “options file” that you can send to another NodeXL user or use yourself for other NodeXL workbooks. Use NodeXL, Options, Export.
• Import an options file into a workbook using NodeXL, Options, Import. (Known bug, will be fixed in next release: The setting for the Scale slider at the top of the graph pane does not get imported.)
• The old “Options” button at the top of the graph pane is now called “Graph Options.”
• There is no longer a Background button in NodeXL, Visual Attributes. The graph’s background color and image are now both set via Graph Options.

For those who are running automated collections that then run automated processing of a workbook (see: How to schedule the creation of a network with NodeXL and Windows Task Scheduler) you should note that:

• The NodeXL Network Server console program now lets you specify a NodeXL options file to use when a network is saved to a NodeXL workbook. See the NodeXLOptionsFile topic in the SampleNetworkConfiguration.xml file for details.  This means that the same machine can be used for scheduled network collection and processing without console users interfering with the settings for automated graphs.

After you click in the graph pane, a number of keyboard shortcuts are now available for functions that had previously been available in the visualization pane’s right-click menu. Now, you can press:

Ctrl+A to select all vertices and edges
Ctrl+V to select all vertices
Ctrl+E to select all edges
Ctrl+D to deselect everything
Ctrl+P to edit the properties of the selected vertices
Ctrl+C to save the graph image to the Windows clipboard
Ctrl+I to save the graph image to a file
Arrow key to move the selected vertices a small distance
Shift+arrow key to move the selected vertices a large distance.

(If you forget a shortcut, most of them are listed in the graph pane’s right-click menu.)

If you have any suggestions for other frequent tasks that could be accelerated with a keyboard command, please contact us on the NodeXL discussion board or here in the comments.

I will present a workshop on social media network analysis at the next Network Singularity event at Fort Mason on the Bay in San Francisco on April 30, 2010.

Bay Area and Silicon Valley Network: Special Action/Research Event

Social Media Network Analysis: Next Practices in Social Network Analysis, Tools and Media

Friday 30 April 2010
8:00am -5:00pm
http://www.regonline.com/BAN10
Workshop Abstract
Why do some social media and online groups succeed when others fail? How do different collections of online media and populations of authors and users differ from one another? How do patterns of contribution vary? How do these differences illustrate the roles people play within their communities?

Patterns of contribution and connection determines social media success. Visualizing these network patterns aids implementation, adoption, security and effectiveness of social media. A range of Internet social media including discussion groups, Twitter, enterprise social media, communities-of-practice, blogs and email are presented, analyzed and visualized. Network patterns are explored to illustrate the scope of variation among social media repositories and between types of contributors.

Maps and patterns of interaction deliver a far more comprehensive view of social media. These views generate actionable findings to improve social media effectiveness, syndication and collaborative outcomes. Network analysis and intelligence can guide community cultivation, coordination and development tasks. New network capabilities provision features that improve search, ranking and consumption of user generated content.

A freely available, open source tool will be demonstrated to perform basic social media network analysis for contemporary social media that should be relevant to business models and organizations of all types. Delegates will be equipped to use social network techniques to improve overall effectiveness and impact of social media, communities-of-practice and social software applications. Participants may expect to achieve immediate improvements in key business activities such as marketing, sales, engineering, support, service, innovation and overall organizational effectiveness.

Directions

The #ashtag network is highlighted by well known technology bloggers.

In contrast the map for the tag “#ashcloud” is dominated by the UK and Scottish travel and Foreign office twitter accounts.

This image features a minor but useful tip for building multi-line tooltips.  This can help pack a lot of information about a Node into the tooltip.  To build a multi-line tooltip use the “&CHAR(10)&” feature to glue other columns of data together along with labels.

Add a column to the Vertices worksheet and paste something like this into it:

=”Twitter user: “&Vertices[[#This Row],[Vertex]]&” – Followers:”&Vertices[[#This Row],[Followers]]&CHAR(10)&”Description: “&Vertices[[#This Row],[Description]]&CHAR(10)&”Last tweet: “&Vertices[[#This Row],[Tweet]]

Which creates a tooltip with multiple lines containing the Twitter User name, the number of their followers, a new line containing their user description followed by their last tweet on another line.  Any concatenation of columns contents can be assembled in this way.

A complete social network map, with edge thickness proportional to the number of network connections (follows, replies, mentions) that link the two participants is here:

The zoomed version highlights the key players in the network and the tighter connections that surround the account for Twitter founder “Ev”.

This is the network map of follows, replies, and mentions relationships among Twitter users who mentioned “CHI2010″ as of  April 12, 2010 scaled by number of followers.

This chart is a log/linear plot of followers (X) over tweets (Y).

Some people tweet a lot in order to get lots of followers, while others can tweet only modestly and gain a vast following.  On a log/log scale this looks like:

The log scales are mislabeled (it is a known, pending work item!), but the pattern is clear: Tweet more and get more Followers.  Some people break from the pack, of course, leading or lagging the core population.  The tooltip is the latest tweet from the most followed Twitter author in the population.

This is a map of the recent connections among twitter users who tweeted the string “NodeXL”.  Each profile photo is scaled to the number of followers that person has.  In this image one author node is right-clicked and reveals some useful options.  Highlighted in the image is the option to open the twitter page for this person.  Also of use is the Edit Selected Vertex Properties option where nodes can have their labels moved around (top, bottom, left, etc) and switched on and off in the graph.

Right-click features appear elsewhere in the application.  It is worth poking at things with the right-click menu but we will be working on surfacing these options more clearing in forthcoming releases.

You can add your own right-click menus to your own networks by using the custom vertex menu option.  Find out how after the jump.

Select the “Customize” button from the “Menus” section:

This leads to:

Select Yes and you will have two new columns in your workbook on the vertices worksheet.  One column contains information that is used in the menu label.  The second column contains any URL that will be executed when the menu item is selected.

If you are attending the CHI 2010 conference in Atlanta and are interested in social media network analysis, consider attending this tutorial:

CN03: Introduction to Social Network Analysis
Time: Monday, 12 April 2010, 11:30 to 18:00
Organizers: Marc A. Smith, C.S. Ang, Derek Hansen, Panayiotis Zaphiris
Presenters: Derek Hansen, Panayiotis Zaphiris

Benefits
This course provides an overview of Social Network Analysis (SNA) and demonstrates through theory and practical case studies how it can be used in HCI (especially computer-mediated communication and CSCW) research and practise. This topic is of particular importance due to the popularity of social networking websites (e.g. YouTube, Facebook, MySpace etc.) and social computing. As people increasingly use online communities for social interaction, new methods are needed to study these phenomena. SNA is a valuable contribution to HCI research as it gives an opportunity to rigorously study the complex patterns of online communication.

Social network theory views a network as a group of actors who are connected by a set of relationships. Actors are often people, but can also be nations, organizations, objects etc. Social Network Analysis (SNA) focuses on patterns of relations between these actors. It seeks to describe networks of relations as fully as possible. This includes teasing out the prominent patterns in such networks, tracing the flow of information through them, and discovering what effects these relations and networks have on people and organizations. It can therefore be used to study network patterns of organizations, ideas, and people that are connected via various means in an online environment.

Audience
We welcome practitioners and academics interested in computer-mediated communication, universal design, and social software. No background knowledge about Social Network Analysis or statistics is required.

Origins
Versions of this course were delivered as tutorials at HCII 2007, 2009, Nordi-CHI 2009, INTERACT 2009, the INSNA Sunbelt Social Network Analysis Conference, the 2009 Communities and Technologies Conference and is scheduled to be run this Summer at the Stanford Media – X workshop and the 2009 Social Computing Conference . The content of the course is also part of the Inclusive Design course at the Cyprus University of Technology and has been taught in the context of a class on Communities of Practice at the University of Maryland, College of Information Studies. The course is constantly being revised and modified to account for current trends in this dynamic and emerging research area.

Features
Upon completion of this course, participants will:

• understand the basics of SNA, its terminology and background;
• be able to transform communication data (e.g. YouTube, MySpace etc.) to network data;
• understand the different possible presentations of social networks, e.g in a matrix or a sociogram;
• know practically how SNA can be applied to HCI (especially CMC) analysis;
• get familiar with the use of standard SNA tools and software in general and the NodeXL social network analysis add-in for Excel in particular; and
• be able to derive practical and useful information through SNA analysis that would help designers cultivate more successful online communities.

Organizers and Instructors

• Dr. Marc A. Smith is a sociologist and Chief Social Scientist at Connected Action Consulting Group, a provider of fine quality social media analysis platforms and systems. Smith specializes in the social organization of online communities and computer mediated interaction. He founded and managed the Community Technologies Group at Microsoft Research in Redmond, Washington and is now leading the development of social media reporting and analysis tools for Connected Action. Smith lives and works in Silicon Valley, California. [ marc@connectedaction.net, http://www.connectedaction.net, http://delicious.com/marc_smith]
• Dr. Panayiotis Zaphiris is the departmental coordinator and an Associate Professor at the Department of Multimedia and Graphic Arts of the Cyprus University of Technology. His research interests lie in HCI with an emphasis on inclusive design and social aspects of computing. More information can be found at http://www.zaphiris.org.
• Dr. C.S. Ang is a Lecturer at Kent University, UK. His research interests include social aspects of computer games, computer-mediated communication and agent-based simulation of social networks.
• Dr. Derek Hansen is assistant professor at the University of Maryland, College of Information Studies and the Director of the Center for the Advanced Study of Communities and Information. Hansen focuses his research and teaching on social computing, mass collaboration, consumer health informatics, and information services.

Travel Industry Social Media Analysis: United v Delta – How do airline twitter maps compare? from Marc Smith on Vimeo.

The two maps we discuss are: United Airlines:

and Delta Airlines:

United has a large twitter mentioning population and has a bigger main component.  The larger profile photos indicate Twitter authors with many followers.  The large population of isolates or small components at the bottom of both images are people who mention either airline but are not in a conversation or a relationship with someone else who also mentioned these brands.  They are “shouts” about a brand, not conversations.  In contrast the large component in both images are the connected collection of people who talk to people who talk about these brands.  They are committed to the topic in a way the less connected  authors are not.  They know someone who also talks about air travel.

Viewed over time, we can start to assess the ways these brand’s twitter populations are changing.  Are new people moving into central hub positions?  Are people who held those positions drifting away?

A key observation is that some people with relatively few followers occupy highly central positions in the graph.  This suggests that these authors have a location that lends their content attention from other well positioned people.  Popularity is not just about volume of connections, in a social network perspective, importance is also a function of where the person sits within the graph.

Princeton Center for Information Technology Policy

I attended the “Studying Society In A Digital World” conference at the Center for Information Technology Policy at Princeton University.  They just posted most of the conference slides.  I took some pictures and have inserted them next to the link for slides where I had a picture (or a good one!).  The conference was very useful and informative: there is a great trend towards sensor driven data sets that, in aggregate, illuminate large complex systems in detailed and surprising ways.

Talks from SenseNetworks and MIT made the vision of a continuous “trail” document assembled by location and biological sensors from every human on earth seem not so outlandish. Samuel Madden from MIT spoke about opportunistic mobile wifi connectivity in moving vehicles.  MIT rebuilt the WiFi stack to enable 13ms associations instead of 13 second associations with an access point.  The result is that a car with such a WiFi card can drive along Boston city streets and exchange about 200KB a minute with open unsecured access points along the way.  Free bandwidth in the city.  What do they do with it?  They stream live telemetry of a fleet of cabs.  The cabs have accelerometers on them and GPS which is reported in almost real time back to a server.  Along with the engine computer’s data, they collect a ton of data about traffic and road surface quality.  They can see changing patterns in the activity levels of the cabs and infer changing activity at businesses.

A major theme of several presentations was crowdsourcing for science, with talks about ebird.org and galaxyzoo highlighting a distinction between sites that enable a group to collect data (ebird) – with the associated issues of data validity — and those sites that enable a group to annotate data (galaxyzoo) that has already been expertly collected.

Lada Adamic: Social Influence and the Diffusion of User-Generated Content

Chris Barrett: Co-evolution of Sociotechnical Networks and Individual Behavior

Kathleen M. Carley: Information & Belief Diffusion Through Social Networks: Empirically Grounded Simulation

Damon Centola: New Theory and Experiments on Diffusion in Social Networks

Pablo Chavez: The Current Policy Debates Over Online Information Practices: Implications for Research in the Digital Age

Nosh Contractor: Digital Traces: An Exploratorium for Understanding & Enabling Social Networks

Nathan Eagle, Michael Macy: Scaling of Sociodynamics

Scott Golder: Temporal Rhythms in Electronic Society: Examples from Facebook and Elsewhere

Eric Horvitz: Through the Lens of a Large Instant-Messaging Network: Planetary-Scale Views on Behavior

Tony Jebara: Learning Networks of Places and People from Location Data

Steve Kelling: eBird: The Long Tail of Community Engagement in the Scientific Process

Jon Kleinberg: Spatial Signatures of On-line Behavior

Robert Kraut: Theory-Based Design of On-Line Worlds

W. Russell Neuman: Social Science and Policy Praxis

Jukka-Pekka Onnela: Using Cell Phones to Study the Large-Scale Structure of Social Networks

Paul Resnick: Understanding Opinion Diversity Preferences Through Field Experiments

Matthew Salganik: Community-Generated and Community-Sorted Information In his presentation Matt made the remarkable connection between deliberative democracy and the cat comparrison site: Kitten Wars.  His talk introduced a model for a kind of Am I Hot or Not for political discussions.  His group built a web site that helped the student community at Princeton set its priorities for student government.  The work has significant implications fo deliberation tools for organizations and enterprises.  Unlike systems that simply encourage users to contribute ideas to a potentially long and never acted upon list, this system forces a comparison task that can be performed in one click but demands implicit contrasts and estimation of value.  The use of the almost adictive “hot or not” style interface (or more accurately, kittenwars)  allows users to decide between, for example, longer hours for the student cafeteria or expanded video rental services, and get presented with their estimate in the context of other’s choices and the opportunity to choose between two things again.  After a population has run through a set of pair-wise contrasts a broader sense of the priorities of the community can be calculated.

In my talk, I focused on the idea that information want not to be free or expensive, rather, information wants to be copied.  Like DNA, the goal of any string of bits is to make a duplicate copy of themselves.  Several technical realities mean that while information may exist on a spectrum from private to public, it only moves in one direction (public) and almost never back.  Once made public on the Internet, even if only for a moment, a photo, document, or other digital object is almost certainly to have been copied, indexed, backed up, or replicated.  All efforts to delete a digital object once widely distributed is like trying to take wine out of water.  This is because all cryptography become brittle over time, most bits end up exposed after they get distributed, and more events trigger widespread distribution of bits than expected (for example, linking a photo, and a location, to a tweet that gets copied to LinkedIn and Facebook, that then appears in an RSS feed and is copied from there to Friend Feed.  As it travels, information looses more of the access controls that initially made it relatively private until it is effectively public.

Marc Smith talks about information at Princeton CITP "Studying Society in a Digital World" Conference - Photo Credit: Scott Golder

Marc Smith: Autobiography, Mobile Social Life-Lagging and the Transition from Ephemeral to Archival Society

Joshua Tauberer: Watching the Watchers: Government Oversight with Civic Hacking

Marshall Van Alstyne: Information, Social Networks and Productivity

Sadly, no picture for Luis Van Ahn’s talk: however, this presentation was a fascinating review of the capcha and re-capcha services and the new direction of providing translation services as language learning games.  Luis Van Ahn invented capcha, felt bad about the cumulative human time wasted by filling out those squiggle word puzzels to get on a web site, and decided to harness capchas to a useful task: text recognition for books.  To translate words from bad scans of books that the OCR software fails to recognize correctly, the garbled data is presented to humans, who, collectively, have translated millions of previously unintelligible words.  Now, his new project is to expand the small user population of bi or multilingual speakers who can translate between languages.  The approach applies the “Mechanical Turk” “human intelligence task” concept to language translation.  His language translation service presents foreign language sentences to users with all dictionary words from a simple translation listed below.  Users click on best word selection beneath each foreign word.  The surprising results: pretty good translations AND users start learning a foreign language!

Luis von Ahn: Human Computation

We got some great questions from the attendees about how they can apply these approaches to their social media investments.  Tools like NodeXL can be helpful for people interested in structural data analysis.  Kate has built tools for performing semantic analysis of social media content over time.

The video is now available on vimeo:

Beyond Buzz: On Measuring a Conversation (Web 2.0 Expo 2009 – Katie Niederhoffer & Marc Smith) from Steffan Antonas on Vimeo.

Princeton Center for Information Technology Policy

Princeton’s Center for Information Technology Policy is hosting a conference on “Studying Society in a Digital World”

Thursday - Saturday, April 23-25, 2009 at Princeton University

http://citp.princeton.edu/front/conference-studying-society-in-a-digital-world/

Here are some standout people and talks at the conference:

Lada Adamic, Michigan

Eric Horvitz, Microsoft: 
Through the Lens of a Large Instant-Messaging Network: Planetary-Scale Views on Behavior

Nathan Eagle, MIT: 
Scaling of Sociodynamics

Michael Macy, Cornell

Scott Golder, HP: 
Temporal Rhythms in Electronic Society: Examples from Facebook and Elsewhere

Jukka-Pekka Onnela, Harvard: 
Using Cell Phones to Study the Large-Scale Structure of Social Networks

Paul Resnick, Michigan

Matt Salganik, Princeton:
Community-Generated and Community-Sorted Information

Edward Castronova, Indiana:
The Emergence of Virtual Money: Results from a Virtual World Experiment

Nosh Contractor, Northwestern

Robert Kraut, Carnegie Mellon: 
Theory-Based Design of On-Line Worlds

Jon Kleinberg, Cornell: 
Spatial Signatures of On-Line Behavior