Curved edges have arrived in NodeXL (v. 174).
Here is a network visualization with all the edges drawn as straight lines:
The same network graph can be drawn with slightly curved edges:
The result is a slight improvement in readability.
To start, the edges curve just a bit. Soon, we will allow user control over the extent of the curvature.
Turn on curved edges by checking the box in the Graph Options dialog accessed from the graph pane.
by Dru!
Can useful observations be made by studying the social media sea one bucket at a time?
NodeXL has data import “spigots” for pulling social networks out of several social media systems including Twitter, YouTube, flickr, and email. Twitter networks of follows and followers, reply and mentions can be extracted based on either a user name or a search string “seed”. There are additional networks inside Twitter: a tie is created whenever two people tweet the same URL, for example, or are connected by tweeting from the same general location. For now, the NodeXL Twitter Data Importer is starting with these three initial twitter “tie-types”.
NodeXL queries are not exhaustive collections of Twitter data, we provide a more modest approach, grabbing a slice of recent content and analyzing that. Twitter has a sea of data, NodeXL is importing something like a study of buckets of ocean water. A recent scientific voyage to the Great Pacific Garbage Gyre, for example, collected hundreds of samples of ocean water as they sailed to the central location of the gyre. Each bucket revealed details about the larger state of the ocean (which does not look good). Simlarly, NodeXL is puling buckets of social media network data from the ocean of twitter and, despite the lack of scale, can do some useful science. In part this is a virtue imposed by necessity – constraints imposed by Twitter (even with a rate limit lifted “whitelisted” account) impose significant limits on what can be squeezed out of the Twitter API. For those who lack access to large data center resources, there are scale limits imposed by the capacities of a desktop/laptop device.
Access to large data sets is certainly a hallmark of the “new era of science” that generates observations not from samples but from exhaustive surveys of data terrains. Small samples miss important phenomena it is argued. The counter argument is that many important phenomena appear in most samples, even small ones.
Using the existing features in NodeXL, I can extract the twitter social network for a small group of user accounts. I can provide the names or ask twitter search to deliver them. Alternatively, a keyword can be used to collect all the users and their connections who recently tweeted containing that term. From this selected sample, several observations can be made:
> Not every keyword is equally connected
> Not every twitter user is equally connected nor are their neighbors
> Selected data extractions can be useful in the absence of a global view
Not all ties are the same. Many networks are collections of multiplex relationships: multiple types of ties link the entities in the graph.
NodeXL now (v.1.0.1.120) supports edge styles so that links can have color, width, and style to distinguish their nature in a network visualization.
Edge Styles include: solid, dash, dot, dash-dot, and so on. Use the new Style column on the Edges worksheet to set the style for each edge.
This is a brief demo video about NodeXL analyzing Twitter social network connections among a group of users who all mentioned the term “digg”.
2009 – November – NodeXL – Demo – Mapping Twitter Social Networks “Digg” from Marc Smith on Vimeo.
This is a good demonstration of several features in NodeXL: social media network importers (in this case from Twitter), the use of a variety of layouts, auto-fill column mappings, and dynamic filters to reveal some important structures, groups and people in the graph.
Here is the source data in a NodeXL workbook: 2009 – November – NodeXL – Twitter Digg 3
Note! Excel has issues with security and workbooks. The easiest way to use that file is to open a blank NodeXL template and use the “Import from older workbook ” feature to pull the data into a workbook that your copy of Excel *can* trust.
Here is an interesting use of a network diagram for data display:
http://sdn.slate.com/features/endofamerica/EOANetwork.htm
Readers Who Like This Apocalypse Also Preferred …
The end-of-America social network.
Slate’s “Choose Your Own Apocalypse” game asked readers to browse
through a list of 144 end-of-America scenarios and choose up to five
that seem most likely to wipe the United States off the map. The
scenarios our readers chose fell into patterns. Those who chose
“Laziness,” for example, were also likely to choose “Decadence” and
“Overpopulation.” Those who selected “Theocracy,” meanwhile, were also
likely to choose “Intelligent Design.”
To help visualize those correlations, we’ve arranged the scenarios
into a social network. Two scenarios are connected if at least 5
percent of users voted for the pair as part of their end-of-America
menus. The less-popular scenarios on our list of 144 tended not to be
associated with any others. What you see here, then, are the 82 that
have at least one connection to another potential America-ending
scenario. Mouse over any icon to see its title and connections.
More
A recent paper makes use of NodeXL to create illustrations of data from connections among twitter users drawn from the United States presidential debates in October 2008. One illustration highlights the major clusters in the network.
Tweet the Debates: Understanding Community Annotation of Uncollected Sources
Authors: Shamma, D.A.; Kennedy, L.; Churchill, E.F.
Source: ACM Multimedia, ACM, Beijing, China (2009)
URL: http://www.acmmm09.org/
Keywords: Twitter; Debates; TV; community; multimedia; social; centrality; network
Abstract:
We investigate the practice of sharing short messages (microblogging) around live media events. Our focus is on Twitter and its usage during the 2008 Presidential Debates. We find that analysis of Twitter usage patterns around this media event can yield significant insights into the semantic structure and content of the media object. Specifically, we find that the level of Twitter activity serves as a predictor of changes in topics in the media event. Further we find that conversational cues can identify the key players in the media object and that the content of the Twitter posts can somewhat reflect the topics of discussion in the media object, but are mostly evaluative, in that they express the poster’s reaction to the media. The key contribution of this work is an analysis of the practice of microblogging live events and the core metrics that can leveraged to evaluate and analyze this activity. Finally, we offer suggestions on how our model of segmentation and node identification could apply towards any live, real-time arbitrary event.
Download: wsm01a-shamma.pdf
At the core of the network are the entities that have the most “eigenvector centrality” which turn out to be the major participants in the debate.