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)).

NodeXL: Automatically Collapse Groups in v.166 with Autofill Columns and Conditional Collapse

26AprMay 7, 2015 By Marc Smith

NodeXL allows users to gather vertices into named collections called “Groups”. This is handy whenever the entities in the network are made up of different types or an algorithm has divided the network into sub-regions based on how densely some vertices connect to one another. The Groups menu is found in the NodeXL>Analysis menu:

Since version v.132 of NodeXL it has been possible to Collapse a group of vertices (See: Expand and Collapse Groups of Vertices with NodeXL v.132). When a group is collapsed all of the vertices within that group are removed from the network graph and replaced with a single vertex with a size proportionate to the number of vertices in the group. A small “+” plus sign indicates that the vertex is a placeholder for a group of vertices.

If the user expands a collapsed group all of the vertices that had been hidden return to positions in the network visualization. The Groups menu has commands for creating, collapsing, and expanding groups.

NodeXL (v.166) now has the ability to automatically collapse or expand any group of vertices conditionally based on any attribute in the workbook using the Autofill Columns feature.

The NodeXL Autofill columns feature allows users to map data elements to display elements. At the bottom of this list (you may need to scroll down to see it) you will now find a new row: Group Collapsed?

There are several network metric attributes for each group that are created when the Find Groups and then the Graph Metrics command has been run on a network in NodeXL:

Selecting one of the data items in the drop down allows you to automatically decide if a group with those attributes will be presented in a collapsed or (default) expanded state. The data about each group include the number of vertices within the group, the number of connections between those vertices, the number of non-unique connections, the number of unique connections among the vertices, the number of self-connections, the number of unique connected components, the number of isolated vertices, the number of vertices in the largest component, the number of edges in the largest component, the maximum and average width of the largest component, and the density of the group.

These metrics allow for the automated processing of the graph to measure each group and apply a test to decide if a group is too dense or populous to be seen in an expanded state.

Fully automatic: NodeXL can build your network graphs hands free

12SepMay 7, 2015 By Marc Smith

In many cases I look at a network graph and apply a series of operations to transform it into a more presentable form. For example, I often calculate graph metrics, use Autofill columns to map data to display attributes like size, color, or shape, create clusters, sub-graph images, and then select the Harel-Koren layout and select the options so that small components get lined up in neat rows at the bottom of the graph. I like the edges to be gray and partially transparent. I often set the font size to a large 24 points because I scale the graph to about 10% of its full size to reduce occlusion.

Carrying out each of these operations once is no problem. Repeat 100 times and there is a problem.

The NodeXL team completed another phase of our automation feature, allowing users to build a refined graph with any set of configuration that can be applied to any number of other networks.

Along with the automated collection system, NodeXL can now generate a regular stream of network graphs from social media sources.

Automatic for the people (who use the latest NodeXL!). Release v.1.0.1.128

08JulMay 7, 2015 By Marc Smith

The NodeXL team has just released a new version (v.1.0.1.128) that contains a new “Automation” feature that allows users to define a collection of operations to perform on their network graphs and invoke the complete set in a single button click AND reuse that configuration on other workbook graphs. In fact, the feature will apply the configuration you define to all the files you specify, allowing easy processing of large collections of network data sets.

This week the feature is partially complete. Users can invoke the merge duplicate edges, calculate graph metrics, auto-fill columns, create sub-graph images, find clusters and show graph. These operations can require as many as dozens of clicks when performed manually. If you have dozens or hundreds of network data sets the result is a daunting case of repetitive strain injury and carpal tunnel syndrome. Instead, with automation, these operations can be carried out orders of magnitude more frequently without much pain!

The next release will feature the complete package which will then include control over the layout and graph options. As a result, automatically generated network visualizations can be produced in a pipeline: users will be able to specify a query using the NodeXL desktop network data collector and then automate the processing of large collections of data sets.

The result should be better analysis of time series data sets that have many “slices”. The feature points the way to additional development work for supporting the comparison between networks to evaluate their evolution.

The REM album “Automatic for the people” takes its title from the motto of Athens, Georgia, eatery Weaver D’s Delicious Fine Foods.

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