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Love this graph - showing Eurovision voting patterns. Quote from the study:
The two most obvious groupings are the countries of the former Yugoslavia (near the top) and the former Soviet Union (near the bottom left). Both of these areas, because of their former political unity, have a lot of cultural similarity4 and many people have familial and ethnic ties to countries other than those they live in. As such, it’s in no way surprising that a lot of points are exchanged within these areas.
More interestingly, this phenomenon doesn’t seem to happen as strongly in Western Europe, where national borders have been stable for longer. Instead, there are a number of strong pairings (Andorra/Spain, Monaco/France, Iceland/Denmark), linked by chains of weaker links. The Scandinavian situation is particularly odd, with a chain of votes going roughly eastward: Norway → Iceland → Denmark → Sweden → Finland → Estonia.
We can also look at the strongest negative links: those countries which appear to never vote for each other. In many cases, these seem somewhat bizarre. I have no idea why Andorrans hate Serbia so much (-6.0), but the data never lies. Is it possible that Moldovans really dislike Maltese pop music (-5.2)?
In one case, however, the reason is clear. On all eight occasions when it was possible for Azerbaijan to award points to Armenia, they have failed to do so. The reason for this is the Nagorno-Karabakh war, a conflict between the two countries which took place immediately after the collapse of the Soviet Union, and which has been at a shaky ceasefire since 1994. While Armenia has on occasion dispensed a few points in the direction of Azerbaijan, the reverse has never occurred, and with good reason. In 2009, it was reported that the 43 Azerbaijanis who texted in votes for Armenia in that year’s contest were summoned to the National Security Ministry to explain their actions.
mewo2.github.com/nerdery/2012/05/20/ive-got-eurosong-feve...
You know that most of the times whenever there is any complex topic is to be explained by the teacher he does it with the help of diagrams, because as a human being we are more friendly with Graphing Calculator of explaining than the oral. This causes the Graphing process in mathematics so important because with it the complex function forms and relations are very well explained. Graphing provides a 2D pictorial representation of any equation in respect of the Cartesian axes and some other factors like Slope of the line and range if inequality is also there in equation.
Graph under a bridge in Bruxelles (BE)
Fujica AZ-1
50mm 1:2.8
Kodachrome 100
Epson 4990 Scanner
:: Ikhaan :: Photo :: Video ::
Not terribly exciting to watch, but that's my attenton brainwaves shown on the LED bar-graph. I'm trying to find the best range for an algorithm for negotiating our CERN Aº app :-)
I've been meaning to tidy up my gems for a while, but didn't realise quite how bad things had got until I ran the new gem graph plugin.
A graph of numbers of people using a free internet vocabulary quiz against date, showing a clear correlation of people wasting time during the working week.
HMS GRAPH (ex U-Boat U 570) at Holy Loch on the completion of a trial trip, passing by a depot ship. In the foreground are the conning towers of HMS STURGEON (left) and HMS TIGRIS whilst submarine P 42 (later renamed HMS UNBROKEN) can be seen in the background.
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Percentiles of AGI, from the latest IRS income tax data release. 2004 was a good year for the top of the distribution.
what my #pearltrees page should look like:
www.pearltrees.com/s243a/graph-package-graphl/id11824609
To show your support please join the pearltrees 2.0 sucks group on pearltrees:
These online tools are coded JavaScript programs which runs on compatible Internet browser. Graphing Calculator is also an online tools uses for graphing of mathematical equations. As we all know every program have its fixed process for solving queries which is implemented in its algorithm and for graphing calculator this algorithm are graphing functions which are distinct for various kind of mathematical equations.
How to combine two graphs on Cacti
If you would like to use this photo, be sure to place a proper attribution linking to xmodulo.com
A figure used in a lecture from JR James at the Department of Town and Regional Planning at The University of Sheffield between 1967 and 1978.
No disease is as number intensive as diabetes. Managing diabetes is all about monitoring, balancing, sacrificing, adjusting, compromising ...
It is vital to maintain as normal a blood sugar as possible to avoid complications. A healthy person typically has blood sugar values in the range 70 to 110. A diabetic would kill to have their blood sugars stay in that range. Sure, we could stay in that range if we ate leaves three times a day, minus the dressing. Try eating leaves for a week. You get the idea. Monitoring, logging and analyzing blood glucose trends is key to managing the disease. I use Sugarstats.com, an awesome website developed and managed by Marston, himself a type 1 diabetic. Analyzing blood sugar trends allows me to see what impact foods have on blood sugar levels, and subsequently lets me tweak my insulin pump settings to achieve optimum control.
There is no cure for diabetes, yet.
From: www.connectedaction.net
Link:
These are the most between Twitter users who recently tweeted the word SOPA when queried on January 12, 2012, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another. The data set starts on 1/12/2012 19:13 and ends on 1/12/2012 19:23 UTC.
Layout created with the "Group Layout" feature of NodeXL which tiles bounded regions for each cluster. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.
A visualization of the network is here: www.flickr.com/photos/marc_smith/6690531909/sizes/l/
Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality
Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111
Top most between users:
@eff
@timoreilly
@meyerweb
@anonopshispano
@buddyroemer
@rob_sheridan
@pietrosantilli
@doropeaton
@boldprogressive
@anonymous_sa
Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 1454
Edges With Duplicates: 434
Total Edges: 1888
Self-Loops: 961
Connected Components: 569
Single-Vertex Connected Components: 534
Maximum Vertices in a Connected Component: 360
Maximum Edges in a Connected Component: 977
Maximum Geodesic Distance (Diameter): 9
Average Geodesic Distance: 3.841127
Graph Density: 0.00078979
Modularity: 0.496889
NodeXL Version: 1.0.1.196
More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/ and here:
www.nodexlgraphgallery.org/Pages/Default.aspx
A gallery of NodeXL network data sets is available here: nodexlgraphgallery.org/Pages/Default.aspx?search=twitter
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) - which is dedicated to open tools, open data, and open scholarship.
Donations to support NodeXL are welcome through PayPal: www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_bu...
The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.
Marc Smith on Twitter.
UX Research at Google predicts that one's offline social network contains ~4-6 groups, with 2-10 people. (see: www.slideshare.net/padday/the-real-life-social-network-v2)
I use some graph visualization and clustering against my Facebook graph to see if my experience agrees with Google's research. See my blog post for more details.
Looks about right...7 clear clusters of people emerge, with little traffic between them.