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

http://facebookimporter.codeplex.com/

I spoke at the University of Michigan, School of Information on October 19th, 2010 about “Charting Collections of Connections in Social Media: Mapping and Measuring Social Media Networks to Find Key Positions and Structures“.

I demonstrated the ways social network data sets can be extracted from social media services like Facebook, Twitter, email, YouTube, and flickr.  These network graphs can reveal information about the “shape” of the population in terms of the presence of sub-groups and communities within the larger population.  In addition, each individual participant is located or positioned within the graph, helping to identify the people who are “core” versus those who are peripheral, as well as those who occupy the position of “bridge” between two otherwise separate groups.

Yahoo! Speaker Series

The Yahoo Speaker Series at the School of Information supports distinguished guest lecturers from the fields of information and technology.

2010 Speaker: Marc Smith
VIDEO: Marc Smith

Marc Smith, chief social scientist with the Connected Action Social Group, presented “Charting Collections of Connections in Social Media: Mapping and Measuring Social Media Networks to Find Key Positions and Structures,” on Tuesday, Oct. 19, 2010.

Smith’s talk was sponsored by the Yahoo! Speaker Series, Michigan Interactive & Social Computing, and the School of Information.

In this talk, Smith described how networks are a data structure common across all social media systems — systems defined by enabling populations to author collections of connections. The Social Media Research Foundation‘s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. Using NodeXL, networks become as easy to create and analyze 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 e-mail networks will be presented, illustrating different patterns created when communities, brands, and controversies are discussed.

Are social media sites data banks, secure repositories of personal assets, or data pimps, soliciting intimate exposure for profit?

I think these services need to choose.  I notice that the setting for who can see what in various systems is in flux.  I can set something to private today and may have to reset it keep it private later.

When I upload content to a site, shouldn’t the expectation be that the deposit is governed by the terms at the time of the contribution?  Why should terms change after I upload?  At least, shouldn’t new rules apply only to new content or content explicitly that has had permissions altered.

Banks do lend out the money I provide them, but only in an anonymous way.  No one knows my dollars are in their mortgage or car loan.  Only legally authorized entities can see my banking records (or so I hope).

Data pimps seem to want to give away anything I give up.  They sell my data as quickly and for as much as possible.

Banks have now developed a reputation that does not make them a great contrast for data pimps, but they still try to represent values like security, confidentiality, and reliability.

I have personally assumed that all data I upload is public.  Only my pictures of my kids have been made “private” and I would not be surprised if those pictures ultimately become public.

Photo credit: cambodia4kidsorg

Here is an example: http://twitpic.com/9rvfq

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.

Bernie recently announced on the SOCNET list that he has updated his script for downloading your Facebook network.

“Features:

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.

Bernie is planning a demo at the Sunbelt social network analysis conference in Italy in 2010.

Bernie is the author of the Facebook chapter in our forthcoming book Analyzing Social Media Networks with NodeXL: Insights from a connected world available from Morgan-Kaufmann in July 2010.

My colleague Prof. Ben Shniederman is positively impressed by these images.  He writes, “Notice how the node layout remains stable as edges are removed, so by the 4th figure the edges can all be followed easily….”.  This is one of the themes he highlights in his paper and presentations about problems and improvements in network graph drawing (see: http://www.cs.umd.edu/hcil/nvss/and in particular http://www.cs.umd.edu/hcil/pubs/presentations/NVSS-3.ppt). Prof. Shniederman’s  5th edition of Designing the User Interface is now available with two full chapters on the website with wordles to open each chapter.

A somewhat related article ran the same day in the Style section on The Celebrity Twitter Ecosystem (http://www.nytimes.com/2009/03/29/fashion/29twitter.html). This image focused on the linkages between well known people using Twitter and, by extension, revealing who they follow and who follows them in the social network.

In the first image no names are associated with the nodes, in the second the names are the major point of the diagram.

The practice of “anonymization” of network graphs may be moot in light of a recent publication mentioned on the Social Network Analysis email list (SOCNET) by Mark Round from QinetiQ of a paper:

De-anonymizing Social Networks – Arvind Narayanan & Vitaly Shmatikov
(http://33bits.org/2009/03/19/de-anonymizing-social-networks/)

which suggests that just publishing the unique pattern of links around an individual is sufficient to identify them in an otherwise anonymized data base.

Abstract:
Operators of online social networks are increasingly sharing
potentially sensitive information about users and their relationships
with advertisers, application developers, and data-mining researchers.
Privacy is typically protected by anonymization, i.e., removing names,
addresses, etc.
We present a framework for analyzing privacy and anonymity in social
networks and develop a new re-identification algorithm targeting
anonymized social-network graphs. To demonstrate its effectiveness on
real-world networks, we show that a third of the users who can be
verified to have accounts on both Twitter, a popular microblogging
service, and Flickr, an online photo-sharing site, can be re-identified
in the anonymous Twitter graph with only a 12% error rate.
Our de-anonymization algorithm is based
purely on the network topology, does not require creation of a large
number of dummy “sybil” nodes, is robust to noise and all existing
defenses, and works even when the overlap between the target network
and the adversary’s auxiliary information is small.

Alan Shussman's personal Facebook egonetwork visualization

Alan used the NetworkX tool and python to build this image of his sub-groups in Facebook.

It does work nicely to highlight the life-stage clusters of relationships that mostly stay inward focused, each school or work experience is a set of relationships that mostly link to themselves.

I will post a version of my own network shortly, but it is more dense and interconnected than this image.  It would be interesting to contrast several egonetworks to see how differently different people network with one another.