NodeXL Pro

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Network Overview Discovery and Exploration for Excel (2007 / 2010 / 2013 / 2016).

The Social Media Research Foundation is dedicated to making tools that help people understand social media and social networks.

We produce NodeXL Basic which is available freely and openly to all.

NodeXL Pro offers advanced features for importing social media data, calculating social network metrics, sentiment analysis, and publishing reports.

NodeXL Pro is licensed to users on an annual basis:

Registration keys will be required to run NodeXL Pro starting in October 2015! 

Contact [email protected] for details!

A range of sample NodeXL networks and reports can be found in the NodeXLGraphGallery!

Your registration fee will feed our talented developers and staff so that they can make more software and services.

Your support keeps our projects, like NodeXL, active and strong.  Please contribute by upgrading to NodeXL Pro.

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NodeXL provides support for social network analysis in the context of a spreadsheet.


NodeXL is a project from the Social Media Research Foundation and is a collaboration among:

NodeXL is an add-in for Excel that supports network overview, discovery and exploration.  The code and application can be found at

NodeXL requires Windows (XP, Vista, 7, 8, 10) and Office (2007, 2010, 2013 or 2016).  Other versions of Excel (like 2008 on Mac, or the older 2003) do not work with NodeXL (sorry!).

A video tutorial for NodeXL can be found at: and

A book guide to NodeXL can be found on Amazon.

Supporting data sets can be found at

Information about NodeXL can often be found on the Connected Action blog (

A recent slide deck describing NodeXL can be found at:

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, facebook, wikis and WWW hyperlinks.

Recent features added to NodeXL include faster metrics calculation, larger data sets, new layouts, scales, axes, and legends.

NodeXL has been downloaded more than 450,000 times and is becoming the easiest path to getting insights from network data.


Social Media Research Related Publications

Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters
Marc Smith, Lee Rainie, Ben Shneiderman, and Itai Himelboim
Pew Research Internet Project, February 20, 2014

Abstract: People connect to form groups on Twitter for a variety of purposes. The networks they create have identifiable contours that are shaped by the topic being discussed, the information and influencers driving the conversation, and the social network structures of the participants. A special analysis by the Pew Research Center and the Social Media Research Foundation of thousands of Twitter conversations finds there are six distinct patterns to the conversational and social structures that take place on Twitter: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversation.

Group-in-a-box Layout for Multi-faceted Analysis of Communities 
Eduarda Mendes Rodrigues, Natasa Milic-Frayling, Marc Smith, Ben Shneiderman, Derek Hansen
IEEE Third International Conference on Social Computing, October 9-11, 2011.
Boston, MA

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 meta-layout 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.

EventGraphs: charting collections of conference connections 
Hansen, D., Smith, M., Shneiderman, B.
Hawaii International Conference on System Sciences. Forty-Forth Annual Hawaii International Conference on System Sciences (HICSS). January 4-7, 2011. Kauai, Hawaii.

Abstract: 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.

Visualizing the Signatures of Social Roles in Online Discussion Groups
Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith.
Journal of Social Structure, Vol 8. 2007.

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
Himelboim, Itai, Eric Gleave, and Marc Smith. 2009
Journal of Computer-Mediated Communication, Vol. 14 (JCMC)

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.

Analyzing (Social Media) Networks with NodeXL 
Smith, M., Shneiderman, B., Milic-Frayling, N., Rodrigues, E.M., Barash, V., Dunne, C., Capone, T., Perer, A. & Gleave, E. (2009)
C&T ’09: Proceedings of the Fourth International Conference on Communities and Technologies. Springer.

Abstract: In this paper we present NodeXL, an extensible 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.

Whither the experts: Social affordances and the cultivation of experts in community Q&A systems
SIN ’09: Proc. international symposium on Social Intelligence and Networking. IEEE Computer Society Press.
Howard Welser, Eric Gleave, Marc Smith, Vladimir Barash, Jessica Meckes.

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.

First steps to NetViz Nirvana: evaluating social network analysis with NodeXL
SIN ’09: Proc. international symposium on Social Intelligence and Networking. IEEE Computer Society Press.
Bonsignore, E.M., Dunne, C., Rotman, D., Smith, M., Capone, T., Hansen, D.L. & Shneiderman, B. (2009)

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.

Do You Know the Way to SNA?: A Process Model for Analyzing and Visualizing Social Media Data [42]
Hansen, D., Rotman, D., Bonsignore, E., Milic-Frayling, N., Rodrigues, E., Smith, M., Shneiderman, B. (July 2009)
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.