View allAll Photos Tagged graph
Photos tagged "sunrise" taken since 2003-1-1.
Their horizontal positions represent the day of the year the photo was taken. The vertical bars are the boundaries between months.
There was a big increase in quantity as we get near to today's date (2005-2-16).
This is the second version of <a href="this graph. In this version I am dimming the photos proportional to the number of submissions for that day, to minimize the effect of increasing submissions during the past year. This makes the seasonal change in sunrise times more apparent.
The vertical position represents the time of day the photo was taken, according to the EXIF data. The horizontal lines are hours, with the thick line in the middle representing 12 noon.
I assume these times are not local, but note the cluster of photos around 6:00 am, rising into spring, and then getting later in the day as the year progresses into Autumn.
The "sunset" version of this graph follows - there are about 5 times more "sunset" tags.--
More stuff by jbum:
From: www.connectedaction.net
Link: www.flickr.com/photos/marc_smith/6871711979/sizes/l/
These are the connections among the Twitter users who recently tweeted the word socbiz when queried on February 8, 2012, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another. The data set starts on 2/9/2012 6:03 to 2/12/2012 11:59 UTC. Green lines are "follows" relationships, blue lines are "reply" or "mentions" relationships.
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 larger version of the image is here: www.flickr.com/photos/marc_smith/6871711979/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:
@dhinchcliffe
@igotan
@sandy_carter
@britopian
@rwang0
@rawn
@itsinsider
@glengilmore
@geoff_deweaver
@cmswire
These are the top word pairs by frequency in the network's tweets:
@socbiz, day, 299
social, business, 205
brand, leverage, 96
referencing, multiple, 96
multiple, brands, 96
ads, convince, 96
convince, consumers, 96
enterprise, architecture, 84
smaller, scale, 84
scale, #socbiz, 84
day, brand, 82
day, collaboration, 80
enterprise, social, 76
social, media, 75
organizations, need, 62
#socbiz, #advertising, 60
hollow, shells, 55
shells, without, 55
without, democracy, 55
democracy, #socbiz, 53
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.
Facebook Open Graph is a great opportunity for brands, that will be able to activate their digital experiences in a customized way for the user, at a level that's unprecedented. They'll be able to reach the user and her connections as well very easily.
Facebook Open Graph is - in fact - "the portable user": it makes the whole social web a part of the Facebook ecosystem.
What can expect for the future?
Digital experiences will be personalized and integrated with each other;
Facebook.com won't be the only Facebook destination and - in the long term - it will loose relative relevance because it will be overwhelmed by Facebook as an ecosystem;
Facebook Connect won't exist anymore because Open Graph will make interaction more immediate and direct;
This chart isn't a see it + know it (at first encounter). You have to live with it for a while to recognize the patterns. While it's not quite there yet, there is some goodness here.
Some metrics you want low, some you want high... and that's fine for these charts when you use them over time.
Then you'll have recognizable patterns to overlay on your graph, like diabetes, and you'll see whether your profile measures up to a typical diabetic profile...
Now consider you're a nurse or doctor seeing today's patient list; who is at risk? Where are the "pain" points. What am I seeing over and over again? Etc...
This is a Social network anlysis from www.linkedinlabs.com/inmaps of my LinkedIn contacts. The color coding corresponds to different groups that I know, and how the tool classifies them. (I will say that it is remarkably correct)
LinkedIN20120610a
Tom's been counting down his inbox from silly heights. Working on averages, we can expect him to finish sometime on 11th July.
Within this data:
* 445 tweets from announcing he's leaving Yahoo to earlier tonight
* 174 beginning with the @ symbol
* 6 tweets containing both an @ and a link
* 56 retweets
* 22 hashtags
* 11 tweets of exactly 140 characters
* 50 Question marks
This is the graph for trendmapper.com from this site:
www.aharef.info/static/htmlgraph/
To be completely accurate. It is the graph from this page:
I have made a graph out of the images to show how the uncanny valley effect worked in my case, with the experiment that I made with Alpha, 4 years ago:
www.flickr.com/photos/alpha_auer/sets/72157605718766594/
(This is almost the same as the one which I uploaded just before, except that I flattened out all the gradients that I had in that one. Seemed a bit too much somehow...)
Graph' en cour de réalisation...
f:11 - 1:40s - ISO 125 - 9mm (24mm) - -RawTherapee - Gimp