Largest ever organizational network analysis shows how social networks drive performance. For years now I have been trying to get the message out to senior executives that effective social networks are critical to business performance. By now that is well understood, in part supported by the large body research and academic literature on how social networks in and across organizations drive results and performance. Now researchers from IBM Research and MIT have undertaken the largest study of its kind ever. BusinessWeek writes that Researchers at IBM and MIT have found that certain e-mail connections and patterns at work correlate with higher revenue production.
The report itself, Value of Social Network — A Large-Scale Analysis on Network Structure Impact to Financial Revenue of Information Technology Consultants, provides detail on their findings – for those interested in these issues it’s well worth a read. In summary, there were four key results: 1. 2. SocialAction. SocialAction is a social network analysis tool that integrates visualization and statistics to improve the analytical process. A journal article about SocialAction was recently published in IEEE Computer Graphics and Applications.
See the full details in the papers below. SocialAction won a VAST Mini-Challenge award for uncovering hidden structure in social networks over time. There are also two recent conference publications about SocialAction! Overview SocialAction aims to help researchers understand their social network data. Although both statistical methods and visualizations have been used by network analysts, exploratory data analysis remains a challenge. Users can For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. Participants Adam Perer, Ph.D., Computer Science (primary contact) Ben Shneiderman, Professor, Computer Science SocialAction in Action SocialAction: Analyzing the Social Network of US Senators on Vimeo. new! Sampling Online Social Networks. Lakers vs. Celtics: Social Media Breakdown #NBA. By Adam Schoenfeld – June 8, 2010 The 2010 NBA Finals are underway with a classic battle between the Lakers and Celtics.
Over 10 million people tuned in to watch game 2 on ABC and we tracked nearly 500,000 Tweets and public Facebook posts mentioning Lakers or Celtics during the game. The series is tied at 1 game a piece with the Celtics taking game 2 on the road. We monitored social media activity for the first two games and broke it down below. Overall, the Lakers were mentioned at a greater rate during both games. Looking at activity over time showed a similar pattern in both games. We pulled down a huge dataset on this topic and we know this is just scratching the surface. If you are interested in taking a crack at the data, we’d love to see what you come up with! Adam is the co-founder and CEO at Simply Measured. “obama. Oxford Chaotic Dynamics (OCD) Network data. This page contains links to some network data sets I've compiled over the years.
All of these are free for scientific use to the best of my knowledge, meaning that the original authors have already made the data freely available, or that I have consulted the authors and received permission to the post the data here, or that the data are mine. If you make use of any of these data, please cite the original sources. The data sets are in GML format. For a description of GML see here. GML can be read by many network analysis packages, including Gephi and Cytoscape. I've written a simple parser in C that will read the files into a data structure.
Data sets Zachary's karate club: social network of friendships between 34 members of a karate club at a US university in the 1970s. Other sources of network data There are a number of other pages on the web from which you can download network data. Growth of a Twitter graph. Twitter connections change over time. We tend to follow more people as we go, but we also remove connections depending our interest and attention span.
At least I do. Since I look at the whole network activity from a very thin slice (a list) I prefer to cure my network, I remove some people to be able to keep up with others. I decided to look at what kind of interest groups emerge as I cure my Twitter social graph . Like the Amazon book network research I did earlier, I was inspired by Valdis Krebs ‘s network analysis research. I used the graph layout program I developed earlier for the Amazon book network research. My Twitter Graph Week 1 Three weeks ago I was following 80 people. My Twitter Graph Week 2 Second week, I follow 118 people and the diagram is more hairy. My Twitter Graph Week 3 Third week, I follow 158 people, more bridges, more dense clusters.
Here is a movie showing the force-directed-graph layout program and me interacting to find who is who. SoNIA Examples. Examples & Gallery We've included here a few examples of animations created by various researchers in different domains. Several of them include the .son file so you can regenerate the same movie. Please contact us if you have something for us to include. NOTE: most of these are .MOV files and will reqiure the QuickTime viewer, and are all from 1 to 20 MB in size, so don't look at them over a dialup connection! Basic Examples Some trivial examples for testing and demonstrating concepts. Dan McFarland's Classroom Networks Dan (one of SoNIA's authors) uses SoNIA to visualize the streaming data he has collected on classroom interactions. James Moody Jim is one of the original collaborators on the SoNIA project, and has created a number of interesting visualizations, including the output from a simulation of social balance process.
SIENA team / Andrea Knecht Simulated transition networks from Knecht Friendship Data generated by SIENA using parameters estimated from the data. Ben Shaw Paul Ingram. You just shared a link. How long will people pay attention? How long is a link “alive” before people stop caring? Does it matter what kind of content it is, or where you shared it? At bitly we see a lot of links, and while every link is special, we’re learning a few general principles that we can share.Let’s take a look at one particular story - Baby otter befriended by orphaned kittens - which was first shared by StylistMagazine on Facebook on Tuesday at 7:12am. If we plot clicks over time for this link, we see: Rate of clicks per 10 minutes on “Baby otter befriended by orphaned kittens”We can evaluate the persistence of the link by calculating what we’re calling the half life: the amount of time at which this link will receive half of the clicks it will ever receive after it’s reached its peak.
For this link the half life was 70 minutes, which captures all the clicks between the grey lines on the graph above. Distribution of half-lifes over four different referrer types. This post brought to you by the bitly science team! Researchers Flood Facebook With Bots, Collect 250GB Of User Data. In an experiment that reveals as much about the people on Facebook as it does about Facebook itself, researchers from the Unversity of British Columbia Vancouver infiltrated the social network with bots and made off with information from thousands of users. Around 250GB of data was stolen during the study, including personal and marketable information, and around three thousand users were targeted. Only one in five of the profiles were flagged by the Facebook Immune System, which clearly needs a boost.
The fake UBC accounts, which they call “socialbots,” were created from a few simple scripts, which submitted a the requisite account information: names, pictures, and status updates were trawled from the open web, eventually producing 102 fairly believable accounts. The intelligence organizing this effort is referred to evocatively as the “botherder” or “adversary.” Next, they sent friend requests to random people. You can download the full report here. Cyber Threats - NetSysLab. From NetSysLab (This project is a collaboration between NetSysLab and LERSSE) More info on the project can be found at LERSSE here. A CBC Radio discussion about our socialbot work, a BBC one, another one on CBC, and yet another two in German on DRK (Deutschlandradio Kaltur) and DRW (Deutschlandradio Wissen). A good summary in an ACM Interactions Magazine cover story: Socialbots: voices from the fronts Our personal and professional lives have gone digital: we live, work and play in cyberspace.
The goal of this project is to understand what makes these systems, online social networks in particular, vulnerable to cyber attacks, and inform new designs that lead to systems less vulnerable to both human exploits (i.e., social engineering) and technical exploits (i.e., platform hacks). Rise of the Socialbots: Large-Scale Infiltration in Online Social Networks Online Social Networks (OSNs) have become an integral part of today's Web. People Yazan Boshmaf Ildar Muslukhov Konstantin (Kosta) Beznosov Talks. Geo-Social Map. Visualisation of Social Networks: Sunbelt Vizard Session Presentation. Social network architecture and the maintenance of deleterious cultural traits. Skip to main page content Journal of The Royal Society Interfacersif.royalsocietypublishing.org Published online before print 26 October 2011 doi: 10.1098/rsif.2011.0555 rsif20110555 + Author Affiliations ↵*Author for correspondence (yeaman@zoology.ubc.ca).
How have changes in communications technology affected the way that misinformation spreads through a population and persists? The Similarity Search Homepage. Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites (9781449388348): Matthew A. Russell. SNAP: Download SNAP. Orbis: A Basis for Systematic Analysis of Network Topologies. Release The latest alpha release (version 0.70a) of Orbis be found here.
Orbis requires Boost 1.32 or later as well as the Boost program_options library. Overview Knowledge of network topology is crucial for understanding and predicting the performance, robustness, and scalability of network protocols and applications. Routing and searching in networks, robustness to random network failures and targeted attacks, the speed of virus spreading, and common strategies for traffic engineering and network management all depend on the topological characteristics of a given network. Research involving network topology, particularly Internet topology, generally investigates the following questions: Generation: can we efficiently generate ensembles of random but realistic topologies by reproducing a set of simple graph metrics? Simulations: how does some (new) protocol or application perform on a set of these realistic topologies? Topology Comparisons Publications Orbis.
Dark Web Terrorism Research : Research : Artificial Intelligence Laboratory : Eller College of Management : The University of Arizona. The Dark Web Project and Forum Portal As part of its Dark Web project, the Artificial Intelligence Lab has for several years collected international jihadist forums. These online discussion sites are dedicated to topics relating primarily to Islamic ideology and theology. The Lab now provides search access to these forums through its Dark Web Forum Portal, and in its beta form, the portal provides access to 28 forums, which together comprise nearly 13,000,000 messages. The Portal also provides statistical analysis, download, translation and social network visualization functions for each selected forum. The GeoPolitical Web Project Interested in accessing the Dark Web Forum Portal? You may request an account by submitting a Username Request form (available at Fill out the form completely to ensure your application is responded to Write down your Username and Password.
Already have an account? Research Goal Return to Parameters Funding. CASOS: Home | CASOS. Graph Models calibrated for Online Social Network Analysis. Updates 11/15/13: v1.2c released, bug fix in coarse fitting for random walk model. 12/17/11: There does not appear to be any problem with the modified Forest Fire fitting code as described in the 12/02/11 update. The update was put up due to the fact that the code did not seem to terminate in the time expected - upon further investigation it seems the code simply takes a very long time to run due to it's recursive nature.
In our case, it has been running for several weeks. Parameter fitting is still being run on our datasets and will be released when finished (an update will be made here) As the modified Forest Fire model was shown to not be very effective in replicating social graph structure in our WWW2010 paper, we recommend users to use alternative models such as modified Nearest Neighbor and/or dK-2. 12/02/11: There is a problem with the code associated with fitting for modified Forest Fire. 11/13/11: Modified Random Walk parameters released. 11/10/11: v1.1 released.
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