NodeXL is extendable. 3rd Party developers have been building data providers that can plug into NodeXL that connect the network visualization tool to sources of network data. We now have three providers of extensions to NodeXL: VOSON for WWW hyperlink networks, the Exchange Spigot for NodeXL for extracting enterprise email networks, and the Facebook Spigot for NodeXL that extracts your own Facebook network for analysis and visualization!
We welcome additional data provider projects! Have a network? Connect it to NodeXL with the simple directions listed here.
Import hyperlink networks into NodeXL with the VOSON System — a web-based software incorporating web mining, data visualisation, and traditional empirical social science methods (e.g. social network analysis, SNA). http://voson.anu.edu.au/node/13#VOSON-NodeXL
Facebook Spigot for NodeXL is a new graph data provider for NodeXL which will allow each user to directly download and import from within NodeXL different Facebook networks.
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.
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 firstname.lastname@example.org.
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.
Top most between users:
Graph Metric: Value
Graph Type: Directed
Unique Edges: 3849
Edges With Duplicates: 324
Total Edges: 4173
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
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!
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
Our paper is about visualizing social media and it describes the visualization of the patterns of connections formed when people tweet about events like conferences and news stories.
EventGraphs are social media network diagrams constructed from content selected by its association with time-bounded events, such as conferences. Many conferences now communicate a common “hashtag” or keyword to identify messages related to the event. EventGraphs help make sense of the collections of connections that form when people follow, reply or mention one another and a keyword. This paper defines EventGraphs, characterizes different types, and shows how the social media network analysis add-in NodeXL supports their creation and analysis. The paper also identifies the structural and conversational patterns to look for and highlight in EventGraphs and provides design ideas for their improvement.
Here is the data set: 20110109-NodeXL-Twitter-HICSS
Graph Metric Value
Graph Type Directed
Unique Edges 243
Edges With Duplicates 71
Total Edges 314
Connected Components 21
Single-Vertex Connected Components 18
Maximum Vertices in a Connected Component 69
Maximum Edges in a Connected Component 307
Maximum Geodesic Distance (Diameter) 8
Average Geodesic Distance 3.081693
Graph Density 0.032967033
As mobile devices become a major method for authoring and consuming social media, location data is increasingly a part of many posts, tweets, check-ins, and messages. Many Twitter clients, for example, can add the user’s current latitude and longitude to the metadata associated with a tweet. Other systems like Facebook Places, Google Latitude and Foursquare encourage users to declare where they are to the world, often passing the information to other social media sites.
Using this location data in network analysis opens up a range of new opportunities. Instead of a person – to – person social network, location data allows people to be linked to places and, by extension, places can be linked to other places based on the patterns of connection people create when located in a particular place. A convergence of network analysis and Geographic Information Systems in underway. A great example of this can be found in this wonderful video from the BBC which demonstrates the idea by mapping the flow of telephone calls, texts, and data around the UK and the wider world.
Now, NodeXL (v.156) has the first of a series of features that will start to approximate the experience displayed in the video by supporting the import of location data about networks and plotting networks onto maps.
For now, we have started importing latitude and longitude data that Twitter makes available. If you check “Add a Tweet column to the Vertices worksheet” in NodeXL, Data, Import, From Twitter Search Network or From Twitter User Network, the Twitter user’s geographical coordinates will be added to the Vertices worksheet when they are available.
Note that not every tweet has a latitude and longitude, in fact many do not (yet). Further, note that not every latitude and longitude is accurate, many are not.
We need to implement more features for better location data support in a NodeXL workbook, but this is a start. We are interested in exploring geospatial networks and Twitter is a great data source. With this data in place we may look into features that emit KML files for exploration in other packages like Google Earth. A nifty Google Earth/Spreadsheet importer can take small sets (400) of location data points in a spreadsheet and export them to a KML file, something we could implement in the future as well. In addition we may be able to connect directly with services like Bing Maps and Google Maps to display connections between nodes with known locations. Metrics that calculate the distance between nodes seem sensible as well.
Location coordinates are the key to a cornucopia of related data about a place. Given a latitude and longitude it is possible to find the name of the city it is located in, look up data about that location in official records as well as resources like Wikipedia. Income, education, property values, weather, photos, and more can be pulled together from just a simple lat/long. All of these attributes could be used to cluster or illustrate the network visualization.
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!