February 4th, 2010 by Marc Smith · No Comments
In the most recent prior release of NodeXL we added new metrics that describe networks in terms of their number of components and the length of paths in those networks. In this release we automate creation of histograms of network metrics. It is useful to see the distribution of attributes like in-degree or betweenness to get a feel for the nature of a network. Building a histogram in Excel is easy, but building seven (one for each of the metrics we create: degree, in-degree, out-degree, betweenness, closeness, eigenvector centrality, and clustering coefficient) is a chore. Doing this repeatedly for several networks is too much work! Now, when you calculate metrics in NodeXL we will create these charts for you and place them on the Overall metrics worksheet.
We will add axis markings and titles soon, making these charts ready to use in a variety of network reports. These histograms will also appear in the Dynamic Filters dialog to guide users as they select segments of the distribution to include or filter out of the displayed network graph.
Other updates:
1.0.1.110 (2010-02-03)
- The Overall Metrics worksheet now includes more information about the degree, in-degree, out-degree, betweenness centrality, closeness centrality, eigenvector centrality, and clustering coefficient metrics when those metrics are computed. The additional information includes the minimum, maximum, average, and median metric values, and a histogram showing the metric value distribution.
- The “Convert Old Workbook” item on the NodeXL, Data, Import menu in the Ribbon is now called “Import from NodeXL Workbook Created on Another Computer.” This menu item can be used to work around the following problem: NodeXL workbooks created on a 64-bit Windows computer cannot be opened directly in Excel on a 32-bit Windows computer, and vice-versa. (If you attempt to do so, you will get an error message whose details include “could not find a part of the path.”)
- A Clear All Worksheet Columns Now button has been added to the Autofill Columns dialog box (NodeXL, Visual Properties, Autofill). Also, you can now clear an individual worksheet column by clicking a button in the dialog box’s Options column.
- Bug fix: On large-font machines, the buttons at the bottom of the Autofill Columns dialog box didn’t fit within the dialog box.
- Bug fix: In some circumstances, vertices were drawn below the bottom of the graph pane and were impossible to see. One such circumstance was when the selection was exported to a new workbook (NodeXL, Data, Export, Selection to New NodeXL Workbook). The graph pane in the new workbook acted as if it were taller than its real height, leading to vertices dropping off the bottom.
Tags: Measuring social media · Metrics · Network metrics and measures · Research · Social Media · Social Network Analysis · Sociology
January 30th, 2010 by Marc Smith · 3 Comments
Along with Professors Ben Shneiderman (Computer Science/Human Computer Interaction Lab) and Derek Hansen (College of Information Studies) from the University of Maryland I am writing and editing a book about analyzing the social media networks that form whenever people link or reply to one another, favorite, rate, read, or edit data about other people or their objects. Social media networks can be analyzed using the methods of social network analysis, the mathematical application of graph and network theory to the social sciences. Using social network analysis collections of connections can be analyzed and compared to identify key people and groups and measure changes over time and following interventions.
I am pleased to announce that we have signed with Elsevier/ Morgan Kaufmann to produce a book: Analyzing Social Media Networks with NodeXL: Insights from a Connected World for a Summer 2010 delivery!
A map of the relationships among the population of people who all tweet a particular keyword can lead to the discovery of the key hubs and influential people in the network. A social network analysis of reply patterns in email collections displays clusters around projects and highlights key people and relationships. Visualizing the connections among your friends in Facebook can reveal the various life stages and communities in which you have participated. When you chart the links between videos and users in YouTube content with interesting network properties is exposed based on well connected content creators and influential commentators. A graph of the individual connections between flickr users illustrates the emergent formation of groups around social networks, locations, and topics.
These kinds of social media network data collection, scrubbing, analysis, and display tasks have historically required a remarkable collection of tools and skills. A great example of the variety of tools that can be used in concert to extract, analyze and display social media networks can be found on Drew Conway’s blog. This is a powerful set of tools for those who can master the demands of python and API interfaces. In contrast, the approach the NodeXL project has taken is to provide an end-user GUI application environment built within the framework of Excel 2007 for performing basic social media network analysis and visualization for non-programmers. The python path is certainly the high road for experts and those with demanding volumes or esoteric data requirements. But for the non-coding user, NodeXL may be one of the easiest ways to both manipulate network graphs and get graphs from a variety of social media sources.
There are already some materials available to guide new users interested in learning about NodeXL, social networks, and social media. A video tutorial for NodeXL demonstrates the extraction of the network of people in twitter who mentioned the term “digg”. A tutorial guide to NodeXL offers a step by step guide to features in the NodeXL toolkit (with supporting data sets). But the book will capture the theory, history, domain and process of social media network analysis in a single volume.
The volume contains a broad introduction to social media, social networks and the operation of the NodeXL application and then features a series of chapters from leading researchers that focus on a particular social media system (email, Facebook, Twitter, YouTube, flickr, Wikis, the WWW hyperlink network) and the networks each contains (replies, friends, follows, subscribes, comments, favorites, edits, links, etc). A final chapter outlines a programmer’s view of the NodeXL code, in contrast to the code-free approach of the remainder of the book.
Our intended audience is the mostly non-programming population that is interested in social media and the techniques of social network analysis. The volume is largely in the form of a how-to guide that readers can follow and replicate all examples. Using your own free and open copy of NodeXL, you will be able to use sample data sets or create similar live queries that map relationships in social media systems.
We have an ambitious production schedule so the book may be on a book store shelf or online retailer search result in summer 2010.
Table of contents…
[Read more →]
Tags: Book · NodeXL · Research · Social Media · Social network · Visualization
January 29th, 2010 by Marc Smith · 1 Comment
NodeXL has updated again (v.1.0.1.109) with new network metrics. The application now calculates path length data for your network, reporting the Maximum Geodesic Distance and the Average Geodesic Distance. The list of overall metrics NodeXL creates includes: Vertices (the number of nodes in the graph), Unique Edges, Edges With Duplicates, Total Edges, Self-Loops (Edges that point back at the node from which they originate), Connected Components (each set of connected nodes that are not connected to another set of nodes), Single-Vertex Connected Components (all the “singletons” of just one node in a component), Maximum Vertices in a Connected Component (the size of the “Giant” component), Maximum Edges in a Connected Component (the density of the “Giant” component), Maximum Geodesic Distance (Diameter) (the longest path that can be uniquely walked through the graph), Average Geodesic Distance (the average distance between two nodes in the graph (compare this to the “six degrees” standard), Graph Density (the density of the complete network).
More metrics and details on existing metrics are on the way!
What metrics do you need?
Tags: Measuring social media · Metrics · Network metrics and measures · Social Interaction · Social Media · Social Network Analysis · Social network · Sociology
January 29th, 2010 by Marc Smith · No Comments
The Annual Meeting of the Israel Internet Association (http://www.isoc.org.il (English)) is being held February 22-23 2010. I will be speaking at this year’s meeting: http://www.isoc.org.il/conf2010/agenda.php?lang=en
The previous year’s conference website is at: http://www.isoc.org.il/conf2009/program.php
The Israel Internet Association is the official Israeli Chapter of the Internet Society. Their annual meeting is a central event of academics (sociologists, psychologists, business and law) as well as industry participants from sectors including mobile cellular companies and internet service suppliers.
My talk title: Analyzing Internet social media: visualizing social networks in (mobile) computer networks
Abstract: Social media systems on the Internet are sociologically interesting: why do some online groups succeed where others fail? How do different collections of online media and populations of authors differ from one another? How do patterns of contribution vary and how do these differences illustrate the roles people play within their communities? Several visualizations of patterns of contribution and connection in a range ofInternet social media including web boards, enterprise social networks services, and personal email are presented to illustrate the range of variation among social media repositories and between types of contributors. These images suggest that a more comprehensive overview of social media can generate sociologically relevant findings, improve community management tasks as well as provide features that can improve search and ranking of user generated content. A freely available tool,
NodeXL, will be demonstrated to perform basic social media analysis tasks. Extending these tools to include mobile social software (“mososo”) data sets is a major new direction. In the not too distant future, mobile devices will possess a range of sensors and become more “socially aware”. When phones routinely notice each other the nature of social interaction will change dramatically. How will places and locations change when machines become socially aware? In this talk, sociologist
Marc Smith, Chief Social Scientist for
Connected Action Consulting Group, a provider of social media analysis platforms and services, will describe these new technologies and some ways of thinking about their implications.
Tags: Conference · Industry · Measuring social media · NodeXL · Research · Social Media · Social network · Talks
January 27th, 2010 by Marc Smith · No Comments
NodeXL updated starting with version 1.05 with features that make it fairly easy to create basic “Venn Diagrams”. A Venn diagram is a familiar way to illustrate the overlap (or lack thereof) of two or more “sets” of things.
There are some very amusing Venn diagrams out there! This one in particular made me laugh but I may be dating myself.
The Venn diagram feature is a special request from the Microsoft Biological Foundation group.
A Venn is related to but different from an Euler diagram. An “n-Venn” diagram is a collection of closed curves (“circles”) on a plane where all the circles intersect. A “simple” Venn diagram has just two circles but complex diagrams can have more. A 2 circle Venn diagram has 3 regions (A, B, A+B) and a 3 circle Venn diagram has 7 regions (A, B, C, AB, AC, BC, ABC).
A Survey of Venn Diagrams can be found at http://www.combinatorics.org/Surveys/ds5/VennEJC.html.
Our implementation is a bit of a hack, we basically let you define the X/Y location of 3 circles. A richer Venn tool would make it easy to take set data and define these circles. We may get that implemented in the coming months.
Tags: Euler Diagrams · NodeXL · Sets · Venn Diagrams · Visualization
January 15th, 2010 by tom · No Comments
Facebook recently announced its fellowship program for graduate students pursuing a PhD. Winners will receive tuition, fees, and a stipend for the 2010-2011 academic year. Anyone interested in applying should move quickly, as the deadlines are quite tight in order to ensure that we can provide funding for the upcoming year.
The fellowship is designed to support research in the following areas:
- Internet Economics: auction theory and algorithmic game theory relevant to online advertising auctions.
- Cloud Computing: storage, databases, and optimization for computing in a massively distributed environment.
- Social Computing: models, algorithms and systems around social networks, social media, social search and collaborative environments.
- Data Mining and Machine Learning: learning algorithms, feature generation, and evaluation methods to produce effective online and offline models of behavioral signals.
- Systems: hardware, operating system, runtime, and language support for fast, scalable, efficient data centers.
- Information Retrieval: search algorithms, information extraction, question answering, cross-lingual retrieval and multimedia retrieval
Applications are due on February 15th. For full details, check out the Facebook Fellowship page.
Please pass this information on to any PhD students you think might be interested in applying.
Tags: Facebook · University
January 14th, 2010 by Marc Smith · 3 Comments
Network visualizations can be very compelling but they are often a smear of unintelligible nodes and edges without refinement and filtering. Creating an automated layout for a complex graph is a challenging area of mathematics and computer science. Several layouts are available and are widely implemented, including the Fruchterman-Reingold layout, the Harel-Koren fast multilevel layout, and a number of geometric designs like circles, grids and trees that can be useful for some data sets.
Improvements to these layouts have been slow in coming: the math behind these layout algorithms have no simple or even best solutions.
Recently, a simple technique has done a great deal to improve many complex network graph layouts by arranging each component in the graph in a grid. Components are pieces of a network that are not connected to any other component. These islands come in various sizes, often there is one large or giant component and many smaller “isolates”. In many layout algorithms these isolates are a problem and are either pushed to the edges of the graph into a circle or ellipse that resembles an “asteroid belt” (Fruchterman-Reingold) or are overlaid on top of all the other isolates (Haren-Koren). A solution is to collect all the “isolates” and organize them sensibly and within a grid such that each component is laid out within its own territory or cell.
*BEFORE*
*AFTER*
The result is a step towards what Ben Shneiderman refers to as “NetViz Nirvana” – a state in which network graphs are more visually intelligible. When isolates are binned in a grid, two graphs can be visually contrasted far more than when they each have a smeared “asteroid belt” of nodes.
We have implemented an initial binning layout method in the latest version of NodeXL that simply breaks out each component and places it within a grid based on the number of nodes and edges in that sub-graph. I can imagine more sophisticated approaches would locate each sub-graph based on a range of attributes.
I think the improvement in network visualization is significant. Isolates no longer impose a big effect on the giant component which often was compacted and compressed as a result of even a single isolate.
Tags: Network visualization layouts · NodeXL · Social network · Visualization
December 19th, 2009 by Marc Smith · No Comments
There are hundreds of conferences sponsored by the ACM on almost every topic related to computing. In some cases the same person will publish a paper in more than one conference, creating a tie between them. Below is a network map application that displays a collection of ACM conferences connected by this authorship tie: http://bosch.informatik.rwth-aachen.de:5080/AERCS/Networks.jsp
The application is a project created by Manh Cuong Pham a graduate student at RWTH Aachen University, Dept. of Databases and Information Systems working with Prof. Ralf Klamma.
This image displays the isolated component that is composed of the “social” conferences in the ACM schedule: CHI, CSCW, DIS, UIST, GROUP, ECSCW, and Interact. The overview illustrates the macro structure of the graph, with the prominent giant cluster of core computer science topics like algorithms, machine learning, and logic. The rows below this cluster are populated by an archipelago of conferences, a few composed of ten to twenty conferences, but most made up of two to five conferences. These are the more marginal topics in the ACM world, in contrast to the conferences at the cores of the giant component.
It would be nice to see the application add additional network display attributes like size, color, shape, edge thickness to indicate conference attributes like papers published, cited, attendees, and sponsors. It is a nice example of the insights network visualizations can bring to a data set and the value of an interactive interface (and a web interface at that!) for investigating complex graphs.
Tags: Aachen · Data Mining · Interdisciplinary · Metrics · Research · Social Media · Social Network Analysis · Social network · University · Visualization · Web Application
December 17th, 2009 by Marc Smith · No Comments
Here is a recent slidedeck that provides an overview of NodeXL and social media network analysis.
The deck illustrates the use of NodeXL to extract several social media networks from systems like twitter and facebook to generate maps of communities and identify people and objects in key locations.
Tags: Collective Action · Connected Action · Data Mining · NodeXL · Research · Social Media · Social network · Twitter · Visualization
December 17th, 2009 by tom · 3 Comments
My colleagues on the Facebook Data Team recently posted the results of a study about the diversity of the Facebook user base.
Using surnames from users in the United States and comparing the rates at which those surnames occur in several ethnic populations, they were able to estimate the proportion of Facebook users within different populations over time. The graphic shows a time series of the estimated percentage of U.S. Facebook users from each of 4 races/ethnicities, normalized by the expected percentage. In other words, a value of 100% means the population in question has the same proportional representation on Facebook as it does in the census data. A value over 100% means the population is over-indexed among Facebook users, and a value under 100% means the population is under-represented on Facebook. It appears that Facebook has always been fairly diverse, and apart from over-indexing for Asians/Pacific Islanders the user population is now reasonably representative of the Internet using population in the U.S.
For more details on the study, including the estimation and modeling approaches used, check out the original note on the data team page, or Cameron Marlow’s full repost. Lars Backstrom, Jonathan Chang, Cameron Marlow and Itamar Rosenn conducted this research.
Tags: Facebook · Measuring social media · Research · Sociology
December 15th, 2009 by Marc Smith · 4 Comments
When the late Peter Kollock and I published Communities in Cyberspace with Routledge in 1999 there were few broadband connections, no iPhones, and little WiFi. Today, there is an ebook version of the book and Amazon sells a version for the Kindle, a device it was hard to even imagine when the book was written. Google lets you browse most of it and search all of it. But the key ideas of the volume: identity, interaction, collective action and emergent order remain relevant in a wireless broadband netbook mobile social network real-time web world. The book is now ten years old.
I. Introduction
Introduction to Communities in Cyberspace, Peter Kollock and Marc Smith
“Since 1993, computer networks have grabbed enormous public attention. The major news and entertainment media have been filled with stories about the “information superhighway” and of the financial and political fortunes to be made on it. Computer sales continue to rise and more and more people are getting connected to “the Net”. Computer networks, once an obscure and arcane set of technologies used by a small elite, are now widely used and the subject of political debate, public interest, and popular culture. The “information superhighway” competes with a collection of metaphors that attempt to label and define these technologies. Others, like “cyberspace,” “the Net,” “online,” and “the web,” highlight different aspects of network technology and its meaning, role and impact. Whichever term is used, it is clear that computer networks allow people to create a range of new social spaces in which to meet and interact with one another.”
More details from the book…
[Read more →]
Tags: Book · Collective Action · Common Goods · Community · Data Mining · Measuring social media · Metrics · Research · Social Media · Social Roles · Sociology
December 14th, 2009 by Marc Smith · No Comments
Here is the Call for Papers for the
Fourth International AAAI Conference on Weblogs and Social Media (ICWSM-10)
May 23-26, 2010
George Washington University, Washington, DC
Sponsored by the Association for the Advancement of Artificial Intelligence
IMPORTANT DATES:
Tutorial Proposals: December 1, 2009
Paper Submission: January 8, 2010
Poster/Demo Submission: January 8, 2010
Paper Acceptance: March 3, 2010
Poster/Demo Acceptance: March 3, 2010
Workshop Submission: March 1, 2010
Camera Ready Copies: March 12, 2010
Featuring a keynote by:
Professor Bob Kraut, CMU,
on “Designing Online Communities from Theory”
Professor Michael Kearns, Computer and Information Science,
Univ. of Pennsylvania,
on “Behavioral Experiments in Strategic Networks”
Speakers in Special Sessions:
- Nicole Ellison, Dept. of Telecommunication,
Information Studies and Media, Michigan State Univ.
- James Pennebaker, Dept. of Psychology, Univ. of Texas, Austin
- S. Craig Watkins, Dept. of Radio, TV and Film, Univ. of Texas, Austin- Don Burke, CIA Directorate of Science and Technology, Intellipedia
- Haym Hirsh, National Science Foundation IIS Division Director
- Macon Phillips, U.S. White House, Head of New Media
Tutorial Speakers will include:
- Jake Hofman, Yahoo! Research,
“Large-scale social media analytics with Hadoop”
- Cindy Chung and James Pennebaker, Univ. Texas,
“Using LIWC to uncover social psychology in social media”
[Read more →]
Tags: Conference · Data Mining · Ecology · Interdisciplinary · Measuring social media · Metrics · Papers · Research · Social Interaction · Social Media · Social network · Sociology · Talks · Technology
December 13th, 2009 by Marc Smith · No Comments
NodeXL has had a rudimentary flickr tag network data spigot for some time but we have just added a number of features to this data importer that makes it much more useful.
You can now select the number of network levels to include, an optional sample image file can be included for each tag, and the dialog now provides feedback as it requests the various parts of the network from Flickr.
The tag network generates maps like the following set of connections among terms related to “sociology”:
Tags: Measuring social media · Metrics · NodeXL · Social Media · Social network · Visualization · flickr
December 13th, 2009 by Marc Smith · No Comments
Tags: Measuring social media · NodeXL · Social Media · Social network · Talks
December 13th, 2009 by Marc Smith · No Comments
The Second Conference on Online Deliberation: Design, Research, and Practice (OD2005/DIAC-2005) was held at Stanford University May 20-22, 2005. From that event there is now a book, Online Deliberation: Design, Research, and Practice, edited by Todd Davies and Seeta Peña Gangadharan (CSLI Publications, November 2009). All content in the book is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License.
I will call out a few of the many interesting chapters, one of which I contributed to:
Chapter 5: Friends, Foes, and Fringe: Norms and Structure in Political Discussion Networks (John Kelly, Danyel Fisher, and Marc Smith, pp. 83-93)
And two from colleagues who report on tools for facilitating political debate and decision making:
Chapter 6: Searching the Net for Differences of Opinion (Warren Sack, John Kelly, and Michael Dale, pp. 95-104)
Chapter 26: Online Civic Deliberation with E-Liberate (Douglas Schuler, pp. 293-302)
The book is a great guide to the many ways computer-mediated interaction technologies are being used to build consensus or tear it apart!
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Tags: Book · Collective Action · Measuring social media · Papers · Politics · Research · Social Media · Social Roles · Social network · Sociology · Stanford · Technology
