NodeXL describes the networks you create: Graph Summary in v.203

09MarMay 7, 2015 By Marc Smith

Here is a map of connections among people who recently tweeted the term “peoplebrowsr”.

“But what does that picture mean?”

I hear this reaction frequently when I show people maps I have made of social media connections.

I often point out that the map and the data can reveal people who occupy important locations in the network as well as emergent clusters and groups.

“So why didn’t you just say so?”

I hear this reaction frequently when I explain what is important about a network.

In NodeXL version 203 we have released a new feature called Graph Summary. Our goal is to “just say so”.

In this version we introduce the basics of automatic captioning. In the NodeXL>Graph menu we now have a “Summary” button:

NodeXL will collect information about the creation and configuration of the network. The dialog box looks like this:

Note that NodeXL>Data>Save Import Details in Graph Summary must be selected in the Import menu for the “Data Import” field to be populated.

Selecting “Copy to Clipboard” will load a copy of these text fields into the buffer. An example of that caption is here:

The graph represents a network of up to 1000 Twitter
users whose recent tweets contained "peoplebrowsr". 

The network was obtained on
Friday, 09 March 2012 at 01:21 UTC. 

There is an edge for each follows relationship. 
There is an edge for each "replies-to" relationship
in a tweet. 

There is an edge for each "mentions"
relationship in a tweet. 

There is a self-loop edge for each tweet that is
not a "replies-to" or "mentions". 

The earliest tweet in the network was tweeted on
Friday, 02 March 2012 at 02:39 UTC. 

The latest tweet in the network was tweeted on
Friday, 09 March 2012 at 00:47 UTC.

The graph is directed.

The graph was laid out using the
Harel-Koren Fast Multiscale layout algorithm.

The edge colors are based on relationship values. 
The vertex sizes are based on followers values.

Overall Graph Metrics:
Vertices: 74
Unique Edges: 172
Edges With Duplicates: 123
Total Edges: 295
Self-Loops: 42
Connected Components: 15
Single-Vertex Connected Components: 13
Maximum Vertices in a Connected Component: 58
Maximum Edges in a Connected Component: 276
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 2.014176
Graph Density: 0.036653091447612
Modularity: 0.288302

Top 10 Vertices, Ranked by Betweenness Centrality:
@peoplebrowsr
@andrewgrill
@traviswallis
@thenickfrost
@jas
@alexbudge
@getmingly
@milener
@jeffreyhayzlett
@johnnosta

The graph's vertices were grouped by cluster using the
Clauset-Newman-Moore cluster algorithm.

More NodeXL network visualizations are here:
www.flickr.com/photos/marc_smith/sets/72157622437066929/
and here:
www.nodexlgraphgallery.org/Pages/Default.aspx

A gallery of NodeXL network data sets is available here:
nodexlgraphgallery.org/Pages/Default.aspx?search=twitter

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation
(www.smrfoundation.org) - which is dedicated to
open tools, open data, and open scholarship.

Donations to support NodeXL are welcome through PayPal:
https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=J5AERGAAN552S

The book, Analyzing social media networks with NodeXL:
Insights from a connected world, is available from Morgan Kaufmann and from Amazon.
http://www.amazon.com/gp/product/0123822297?ie=utf8&tag=conneactio-20&linkcode=as2&camp=1789&creative=390957&creativeasin=0123822297

This caption will expand in our next several releases to include information about the top URLs, hashtags, and @usernames in text fields associated with nodes and edges. Following that we will release a series of features to allow for the extraction of keyword pairs in those text fields (our current version of this feature is described here: Keyword Networks: create word association networks from text with NodeXL (with a macro)).

Keyword Networks: create word association networks from text with NodeXL (with a macro)

29JanMay 7, 2015 By Marc Smith

This is the collection of keyword pairs that appeared in two clusters of people who Tweeted about “Paul Ryan”, the Republican Congressman from Wisconsin who delivered the GOP rebuttal to the 2011 United States State of the Union Address. This network illustrates the ways that certain word pairs appears only or predominantly in one cluster (colored here Red and Blue) or the other. Terms that appeared in both clusters appear as purple.

Social networks are built from relationships between people. Keyword networks are built from relationships between words and other text strings. When two words appear in the same message, sentence, or alongside one another ties of different strengths are created. The networks that result can illuminate the relationships among topics of importance in a collection of messages.

Markus Strohmaier from the Technical University Graz (TUG) along with Claudia Wagner gave us inspiration in a paper:

C. Wagner, M. Strohmaier, The Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from Social Awareness Streams, Semantic Search 2010 Workshop (SemSearch2010), in conjunction with the 19th International World Wide Web Conference (WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010. (pdf)

in which they defined a range of ways two words (technically these are strings, they may not really be words) can be associated with one another. Words could be linked if they are in the same tweet, next to one another, or sequential among other ways to link terms.

NodeXL has not had any features for exploring the networks in texts. Now with the addition of a new macro from Scott Golder, it is fairly simple to extract pairs of keywords from collection of tweets. NodeXL’s Twitter importer can optionally include the content of the tweet that included the search term and this column of text can now be processed itself into a new network based on the ways words appear together in tweets.

This feature builds on the work of several people. Scott Golder from Cornell started the ball rolling with a simple but effective VBA script that allowed others to build and refine the models of what counts as a tie between two words. Vladimir Barash added several refinements including support for stop word lists to remove common terms. Scott then picked up the code again and added a set of features for selecting the nature of the graph and making it easier to select the options needed.

The code for the Keyword Network macro is below.

The instructions to use it take a few steps to complete:

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Book: Analyzing Social Media Networks with NodeXL: Insights from a connected world now available

13SepMay 7, 2015 By Marc Smith

In Stock!

The book Analyzing Social Media Networks with NodeXL: Insights from a connected world is now available [Amazon] from Morgan-Kaufman. Co-authored by Professor Derek Hansen (College of Information Studies) and Professor Ben Shneiderman (Computer Science/Human Computer Interaction Lab) from the University of Maryland and Marc Smith from Connected Action, the book is a introduction and guide to the application of social network analysis to social media. The introductory chapters introduce the history and concepts of social network analysis an the varieties of social media, highlighting the presence of a common data structure, the network, in otherwise diverse social media systems including email, Twitter, Facebook, the WWW, Wikis, Blogs, flickr, an YouTube. The central section of the book reviews a step-by-step guide to using the key features of NodeXL, the free and open social media network analysis add-in for Excel 2007 and 2010. Readers can move from simple hand entered networks of a few nodes up to complex graphs extracted from a variety of social media services. The remainder of the book are focused chapters dedicated to analyzing the networks found within a specific social media service. These chapters were contributed by leading social media researchers and illustrate the insights that can be extracted from the otherwise disorganized stream of messages, tweets, posts, comments, links, likes, tags, friends, follows, mentions, replies and ratings. A recent article about the book can be found on the Morgan-Kaufmann website.

Table of contents…

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