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
The widely used Microsoft Exchange email server contains many networks that are created as people reply to one another and join various groups and distribution lists.
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.
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
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.
It provides a good illustration of the ways a person’s social network is clumped into clusters built around life phases, workplaces, educational institutions, teams and locations. As people move through more of these stages of life during the Facebook era (and often before) they accumulate these clusters.
Facebook or other contact and friend management systems might could leverage this clustering to organize the presentation of contact information streams.
1. Its faster. (Presently orders of magnitude faster than Nexus, Touchgraph or ORA).
2. It gives nice feedback during the download.
3. It has less bugs!
4. It gives you the output as a file you can right-click and save rather than copy-paste.
5. IDs are names.”
Bernie writes that phase two of his project is underway.
We have several data import providers (spigots) in NodeXL that query popular sources of social media for information that can be processed into a network graph. User and search term networks from Twitter, YouTube, and flickr have been implemented for a while along with a connector to email reply networks through the Windows Search Index. NodeXL also imports data from several popular network analysis file formats, opening up data sets and sample libraries used in many courses.
NodeXL has had a rudimentary flickr tag network data spigot for some time but we have just added a number of features to this data importer that makes it much more useful.
You can now select the number of network levels to include, an optional sample image file can be included for each tag, and the dialog now provides feedback as it requests the various parts of the network from Flickr.
The tag network generates maps like the following set of connections among terms related to “sociology”: