AEA365 | A Tip-a-Day by and for Evaluators

TAG | network analysis

I’m Marc Smith, Chief Social Scientist at Connected Action, co-founder of the Social Media Research Foundation, and one of the team behind NodeXL I’ve been excited to see posts on aea365 from users of NodeXL such as Johanna Morariu, and Shelly Engleman and Tom McKlin.

Rad Resource – The Social Media Research Foundation: A new organization, the Social Media Research Foundation, has been formed to develop open tools and open data sets, and to foster open scholarship related to social media. The Foundation’s current focus is on creating and publishing tools that enable social media network analysis and visualization from widely used services like email, Twitter, Facebook, flickr, YouTube and the WWW.

Rad Resource – NodeXL: The SMR Foundation has released the free and open NodeXL project, a spreadsheet add-in that supports “network overview discovery and exploration”. The tool fits inside your existing copy of Excel in Office 2007 or 2010 and makes creating a social network map similar to the process of making a pie chart. Researchers applying NodeXL to a range of social media networks have already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks can be seen on the site.

Using NodeXL, users can easily make a map of public social media conversations around topics that matter to them. Maps of the connections among people who recently said the name of a program, organization, or event can reveal people who occupy key positions as well as clusters in the crowd. Some people who talk about a topic are more in the “center” of the graph, they may be influential members of the population. NodeXL makes it a simple task to sort people in a population by their network location.  NodeXL supports exploration of social media with import features for personal email, Twitter, Flickr, YouTube, Facebook and WWW hyper-links.

As an example, I built on Johanna’s post focusing on twitter users of the hashtag #eval. Here is a network map, built directly in NodeXL, with vertices sized by the number of followers.

NodeXL #eval hashtag map

Rad Resource – Analyzing Social Media Networks with NodeXL: Insights from a connected world. My co-authored book provides an introduction to the history and core concepts of social network analysis along with a series of step-by-step instructions that illustrate the use of key features of NodeXL. The second half of the books is dedicated to chapters by a number of leading social media researchers that each focus on a single social media service and the networks it contains. Chapters on Twitter, email, YouTube, flickr, Facebook, Wikis, and the World Wide Web illustrate the network data structures that are common to all social media services.

Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to . aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.

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I’m Johanna Morariu, a Director at Innovation Network, an evaluation consulting firm that works with nonprofit organizations and foundations.

Rad Resources: I want to share two extremely useful network analysis and mapping tools: Gephi and NodeXL. I use NodeXL for collecting, organizing, and analyzing network data and Gephi for attractively presenting sociograms or network maps.

In a past post, Shelly Engelman and Tom McKlin introduced NodeXL when they wrote about assessing the evolution of Social Networks Using NodeXL. In addition to the functionality they discussed, NodeXL can also be used to collect social network data from Twitter, Flickr, YouTube, and email, and NodeXL can open/read other network analysis file formats. (And with the recently released Social Network Importer, you can also work with Facebook social networks.) But for all its strengths and utility, the network maps that can be made in NodeXL leave something to be desired.

Lessons Learned: Visually observing network structural features is a critical component of network analysis. And for evaluation stakeholders to effectively discern features, it is important to create well-designed network maps—and that is exactly what Gephi does!

Gephi allows for unprecedented control and options while creating network maps. For example, groups of network nodes can be coded by color, or degree can be represented by increasing node size. Gephi also has the capacity to incorporate longitudinal data, to show changes over time.

Since a picture is worth a thousand words, here is the progression from NodeXL to Gephi drawn from a twitter search of the hashtag #eval on January 17, 2011.

First, the basic NodeXL map:

 the basic NodeXL map

After exporting the data from NodeXL to a GraphML file, uploading to Gephi, and tweaking, here is the new map:

And with another simple change (turning on automatic resizing by node degree), voilà!

Rad Resources: And since this is the DVRTIG week, I can’t help but share three other essential tools for creating visually appealing presentations:

  • Design Seeds for color palette inspiration.
  • Instant Eyedropper to get RGB values (for example, from a Design Seeds color palette) to use in visualizations.
  • Color Oracle to simulate color blindness to ensure visualization and design retain their meaning for every viewer.

Rad Resources: Interested in learning more about network analysis? Check out these great posts:

We’re celebrating Data Visualization and Reporting Week with our colleagues in the DVR AEA Topical Interest Group. The contributions all this week to aea365 come from our DVR members and you may wish to consider subscribing to our weekly headlines and resources list where we’ll be highlighting DVR resources. Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice.


Hello! I am Maryann Durland and I own an independent consulting firm, Durland Consulting and have been using Network Analysis (NA) since the early 1990’s and hence my focus on our evaluation network. My post today is about the methodology of NA in evaluation applications.  First what is it, second how do you do it, and three what does it look like.

Hot Tip: Network analysis is the methodology for studying relationships among and between members of a set(s).  A set can be people, references, roads and towns, organizations, and so on. Relationships defined for a set can be at three levels –  individuals, subgroups or the whole set, and from a variety of contexts, such as friendships, co-membership in groups, related to and how, work with, readers of the same book, etc. To apply network analysis requires three components:

  1. Define the Network and the Relationship: In some applications the network is self defining – members of an extended family.
  2. Measures Used to Analyze the Data: The choice of measures is usually based on a theory about the relationship.
  3. The Sociogram: The sociogram illustrates the network and also allows us to see the position of individuals within the network to further understand the analysis and which may also indicate further data analysis.
generation shape color
great circle orange
grand square pink
parent triangle blue
child box x green

As an example, in family money exchanges, the data might indicate that dad loans to more people, but daughter 2 is engaged in larger loans. In the sociogram we see how location is important.  The sociogram nodes’ size is related to the amount of money and shape and color are by generation.

We see that dad and mom have similar locations in the network and, except for son 2, they connect to different individuals, suggesting further analysis. Though daughter 2 is connected less as measured by outdegree, she is involved in larger loan amounts. Son 2 is in a pivotal location in the network, bridging mom and dad’s subgroups, indicating that subgroup membership might be another level of analysis.

This provides a small glimpse into network analysis. What I like about network analysis is that it forces you to focus on how we assume behavior will play out in our initiatives like what do we assume mentors will do in a mentoring relationship? And it allows us to explore the complexity of our programs and initiatives.  It requires thinking about the systems within which our initiatives are situated and it is fun.

Rad Resource: Want to learn more about Social Network Analysis? An introductory text is available online at

This contribution is from the aea365 Daily Tips blog, by and for evaluators, from the American Evaluation Association. Please consider contributing – send a note of interest to

family borrowing3.jpg

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