Network Overview Discovery and Exploration for Excel 2007/2010
NodeXL provides support for social network analysis in the context of a spreadsheet.
- Connected Action Consulting Group
- Microsoft Research
- University of Maryland
- Cornell University
- Stanford University
- Oxford Internet Institute
NodeXL requires Office 2007, 2010 or 2013. Other versions of Excel (like 2008 on Mac, or the older 2003) do not work with NodeXL (sorry!). Mac users may want to run NodeXL in a virtual machine locally or in the cloud.
A recent slide deck describing NodeXL can be found at: http://slideshare.net/Marc_A_Smith/2013-nodexl-social-media-network-analysis
NodeXL allows for the import of network data in the form of edge lists, matricies, graphML, UCINet, and Pajek files along with CSV and other workbooks.
NodeXL allows non-programmers to quickly generate useful network statistics and metrics and create visualizations of network graphs. Filtering and display attributes can be used to highlight important structures in the network.
NodeXL supports the exploration of social media with import features that pull data from personal email indexes on the desktop, twitter, flickr, youtube, and, soon, facebook and WWW hyperlinks.
Recent features added to NodeXL include faster metrics calculation, larger data sets, new layouts, scales, axes, and legends.
Social Media Network Research Related Publications
In the Journal of Social Structure: “Visualizing the Signatures of Social Roles in Online Discussion Groups” is available from: http://www.cmu.edu/joss/content/articles/volume8/Welser/It illustrates different patterns of network structures associated with different kinds of roles and behaviors.
Abstract: Social roles in online discussion forums can be described by patterned characteristics of communication between network members which we conceive of as ‘structural signatures.’ This paper uses visualization methods to reveal these structural signatures and regression analysis to confirm the relationship between these signatures and their associated roles in Usenet newsgroups. Our analysis focuses on distinguishing the signatures of one role from others, the role of “answer people.” Answer people are individuals whose dominant behavior is to respond to questions posed by other users. We found that answer people predominantly contribute one or a few messages to discussions initiated by others, are disproportionately tied to relative isolates, have few intense ties and have few triangles in their local networks. OLS regression shows that these signatures are strongly correlated with role behavior and, in combination, provide a strongly predictive model for identifying role behavior (R2=.72). To conclude, we consider strategies for further improving the identification of role behavior in online discussion settings and consider how the development of a taxonomy of author types could be extended to a taxonomy of newsgroups in particular and discussion systems in general.
“Discussion catalysts in online political discussions: Content importers and conversation starters“ in the Journal of Computer-Mediated Communication (JCMC) http://jcmc.indiana.edu/athttp://ping.fm/7NF5T
Abstract: This study addresses 3 research questions in the context of online political discussions: What is the distribution of successful topic starting practices, what characterizes the content of large thread-starting messages, and what is the source of that content? A 6-month analysis of almost 40,000 authors in 20 political Usenet newsgroups identiﬁed authors who received a disproportionate number of replies. We labeled these authors ‘‘discussion catalysts.’’ Content analysis revealed that 95 percent of discussion catalysts’ messages contained content imported from elsewhere on the web, about 2/3 from traditional news organizations. We conclude that the ﬂow of information from the content creators to the readers and writers continues to be mediated by a few individuals who act as ﬁlters and ampliﬁers.
Smith, M., Shneiderman, B., Milic-Frayling, N., Rodrigues, E.M., Barash, V., Dunne, C., Capone, T., Perer, A. & Gleave, E. (2009),”Analyzing (Social Media) Networks with NodeXL“, In C&T ’09: Proceedings of the Fourth International Conference on Communities and Technologies. Springer.
Abstract: In this paper we present NodeXL, an extendible toolkit for network data analysis and visualization, implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL features through analysis of a data sample drawn from an enterprise intranet social network, discussion, and wiki. Through a sequence of steps we show how NodeXL leverages and extends the broadly used spreadsheet paradigm to support common operations in network analysis. This ranges from data import to computation of network statistics and refinement of network visualization through a selection of ready-to-use sorting, filtering, and clustering functions.
Howard Welser, Eric Gleave, Marc Smith, Vladimir Barash, Jessica Meckes. “Whither the Experts? Social affordances and the cultivation of experts in community Q&A systems”, in SIN ’09: Proc. international symposium on Social Intelligence and Networking. IEEE Computer Society Press.
Abstract: Community based Question and Answer systems have been promoted as web 2.0 solutions to the problem of finding expert knowledge. This promise depends on systems’ capacity to attract and sustain experts capable of offering high quality, factual answers. Content analysis of dedicated contributors’ messages in the Live QnA system found: (1) few contributors who focused on providing technical answers (2) a preponderance of attention paid to opinion and discussion, especially in non-technical threads. This paucity of experts raises an important general question: how do the social affordances of a site alter the ecology of roles found there? Using insights from recent research in online community, we generate a series of expectations about how social affordances are likely to alter the role ecology of online systems.
Bonsignore, E.M., Dunne, C., Rotman, D., Smith, M., Capone, T., Hansen, D.L. & Shneiderman, B. (2009), ”First steps to NetViz Nirvana: evaluating social network analysis with NodeXL“, In SIN ’09: Proc. international symposium on Social Intelligence and Networking. IEEE Computer Society Press.
Abstract: Social Network Analysis (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners and students alike. NodeXL, an open-source template for Microsoft Excel, integrates a library of common network metrics and graph layout algorithms within the familiar spreadsheet format, offering a potentially low-barrier to-entry framework for teaching and learning SNA. We present the preliminary findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. The majority of students, while information professionals, had little technical background or experience with SNA techniques. Six of the participants had more technical backgrounds and were chosen specifically for their experience with graph drawing and information visualization. Our primary objectives were (1) to evaluate NodeXL as an SNA tool for a broad base of users and (2) to explore methods for teaching SNA. Our complementary dual case-study format demonstrates the usability of NodeXL for a diverse set of users, and significantly, the power of a tightly integrated metrics/visualization tool to spark insight and facilitate sensemaking for students of SNA.
Hansen, D., Rotman, D., Bonsignore, E., Milic-Frayling, N., Rodrigues, E., Smith, M., Shneiderman, B. (July 2009)
Do You Know the Way to SNA?: A Process Model for Analyzing and Visualizing Social Media Data
University of Maryland Tech Report: HCIL-2009-17
Abstract: Voluminous online activity data from users of social media can shed light on individual behavior, social relationships, and community efficacy. However, tools and processes to analyze this data are just beginning to evolve. We studied 15 graduate students who were taught to use NodeXL to analyze social media data sets. Based on these observations, we present a process model of social network analysis (SNA) and visualization, then use it to identify stages where intervention from peers, experts, and computational aids are most useful. We offer implications for designers of SNA tools, educators, and community & organizational analysts.