Abstract: Networks are everywhere except the end user desktop. NodeXL, the free and open network overview, discovery and exploration add-in for the popular and familiar Excel (2007/2010) spreadsheet allows users who are comfortable making pie charts to now make useful network visualizations. Developed and released by the Social Media Research Foundation, NodeXL uses Excel as a framework, providing a GUI network browser (a “web browser”?) that novices can use quickly and experts can use to generate sophisticated results. Data importers provide access to a range of social media network data sources like Twitter, flickr, YouTube, Facebook, email, the WWW, and more through standard file formats (CSV, GraphML, Matrix). Simple to use tools can automatically analyze, visualize and highlight insights in complex network graphs. Using NodeXL, researchers have been collecting a wide range of network data sets from various social media services. These images reveal a range of common social formations in social media and point to people who occupy strategic locations in these graphs.
This is a map of the connections among the people who tweeted the term “PAWCON” on the first day of the event:
These are the connections among the Twitter users who recently tweeted the word #pawcon when queried on October 19, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
Top most between users:
Graph Metric: Value
Graph Type: Directed
Unique Edges: 233
Edges With Duplicates: 120
Total Edges: 353
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 40
Maximum Edges in a Connected Component: 352
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 1.87133
Graph Density: 0.15304878
NodeXL Version: 188.8.131.52
Here is an example map of the connections among the people who tweeted the term “pawcon” in Twitter on September 14th, a week prior to the event.
Manu Sharma, Principle Research Scientist at LinkedIn gave a great presentation on the patterns found in their data. Big data, for example, showed that most of the people who previously worked at recently failed banks and financial institutions have updated their profiles to show that they mostly have new jobs at some of the remaining companies in the industry.
Abstract: Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a metalayout for clustered graphs that enables multi-faceted analysis of networks. It uses the treemap space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.
The paper is authored by:
Eduarda Mendes Rodrigues*, Natasa Milic-Frayling†, Marc Smith‡, Ben Shneiderman§, Derek Hansen¶
* Dept. of Informatics Engineering, Faculty of Engineering, University of Porto, Portugal – eduardamr @ acm.org
† Microsoft Research, Cambridge, UK -natasamf @ microsoft.com
‡ Connected Action Consulting Group, Belmont, California, USA – marc @ connectedaction.net
§ Dept. of Computer Science & Human-Computer Interaction Lab, University of Maryland, College Park, Maryland, USA – ben @ cs.umd.edu
¶ College of Information Studies, University of Maryland, College Park, Maryland – dlhansen @ umd.edu
A map of the connections among the people who recently tweeted #SocialCom2011:
Connections among the Twitter users who recently tweeted the word #socialcom2011 when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.
Our paper is about visualizing social media and it describes the visualization of the patterns of connections formed when people tweet about events like conferences and news stories.
EventGraphs are social media network diagrams constructed from content selected by its association with time-bounded events, such as conferences. Many conferences now communicate a common “hashtag” or keyword to identify messages related to the event. EventGraphs help make sense of the collections of connections that form when people follow, reply or mention one another and a keyword. This paper defines EventGraphs, characterizes different types, and shows how the social media network analysis add-in NodeXL supports their creation and analysis. The paper also identifies the structural and conversational patterns to look for and highlight in EventGraphs and provides design ideas for their improvement.
Here is the data set: 20110109-NodeXL-Twitter-HICSS
Graph Metric Value
Graph Type Directed
Unique Edges 243
Edges With Duplicates 71
Total Edges 314
Connected Components 21
Single-Vertex Connected Components 18
Maximum Vertices in a Connected Component 69
Maximum Edges in a Connected Component 307
Maximum Geodesic Distance (Diameter) 8
Average Geodesic Distance 3.081693
Graph Density 0.032967033
Businesses, entrepreneurs, individuals, and government agencies alike are looking to social network analysis (SNA) tools for insight into trends, connections, and fluctuations in social media. Microsoft’s NodeXL is a free, open-source SNA plug-in for use with Excel. It provides instant graphical representation of relationships of complex networked data. But it goes further than other SNA tools—NodeXL was developed by a multidisciplinary team of experts that bring together information studies, computer science, sociology, human-computer interaction, and over 20 years of visual analytic theory and information visualization into a simple tool anyone can use. This makes NodeXL of interest not only to end-users but also to researchers and students studying visual and network analytics and their application in the real world. NodeXL has the unique feature that it imports networks from Outlook email, Twitter, flickr, YouTube, WWW, and other sources, plus it offers a rich set of metrics, layouts, and clustering algorithms. This talk will describe NodeXL and our efforts to start the Social Media Research Foundation.
Hello! Social media network maps reveal the key people, groups, and topics discussed in a public conversation.
If you would like to request a custom social media network map made with NodeXL for the topic, hashtag, URL, or username of your choice complete the form below. I will generate the maps as requests come in and email you a pointer to the results which I will post to the NodeXL Graph Gallery: See – https://nodexlgraphgallery.org/Pages/Default.aspx
CustServ Twitter Connections for early January 2014
The visualization represents the connections among 1699 Twitter users over a 2-day, 21-hour, 48-minute period from Wednesday, 08 January 2014 at 02:53 UTC to Saturday, 11 January 2014 at 00:42 UTC.
In the sample map above for the term “CustServ” the visualization represents the connections among 1699 Twitter users over a 2-day, 21-hour, 48-minute period from Wednesday, 08 January 2014 at 02:53 UTC to Saturday, 11 January 2014 at 00:42 UTC.
The most central and possibly “influential” contributors to this discussion are:
For those in the Bay Area there is an event next week of particular interest to community managers, platform developers, researchers, marketers, and software developers: at the Computer History Museum in Mountain View on June 10th is the 2009 Online Community Unconference. This year 300-400 online community and social media professionals will attend with 50-60 collaborative sessions devoted to social media and online community topics.
“The Online Community Unconference is a gathering of online community practitioners – managers, developers, business people, tool providers, investors – to discuss experience and strategies in the development and growth of online communities.”
I attended last year and found it to be a valuable use of time: many practioners in the social media space will attend and some will self-nominate and present on a wide range of topics of relevance. I plan to be there again this year, hope to see you there!