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antropologo.net, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
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Social Network Analysis: New in Mathematica 9 | Wolfram | #SNA #dataviz

Social Network Analysis: New in Mathematica 9 | Wolfram | #SNA #dataviz | e-Xploration | Scoop.it
Complete set of state-of-the art social network analysis functions. High level functions for community detection, cohesive groups, centrality, and similarity measures.
luiy's insight:

Drawing on Mathematica's strong graph and network capabilities, Mathematica 9 introduces a complete and rich set of state-of-the art social network analysis functions. Access to social networks from a variety of sources, including directly from social media sites, and high level functions for community detection, cohesive groups, centrality, and similarity measures make performing network analysis tasks easier and more flexible than ever before.

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Appel à participation Futur en Seine 2014 aux Rencontres #R&D

Appel à participation Futur en Seine 2014 aux Rencontres #R&D | e-Xploration | Scoop.it
Venez Faire et produire avec nous le contenu du festival de toutes les innovations numériques : nouvelles industries, nouveaux produits, nouveaux services, nouveaux médias, nouvelles cultures.
luiy's insight:

Venez Faire et produire avec nous le contenu du festival de toutes les innovations numériques : nouvelles industries, nouveaux produits, nouveaux services, nouveaux médias, nouvelles cultures.

Nous sommes particulièrement intéressés cette année par la fabrication avec le numérique : les Fablabs en entreprises, les nouvelles formes de production locales, la fabrication à la demande, les objets connectés, les Datas, l'allongement de la vie, la ville numérique, l'innovation sociale, la santé, l'éducation, mais aussi les jeux, la musique, le nouveau cinéma, les nouvelles images, la télévision, la radio, le transmédia.

 

Vos projets pourront prendre tous types de formes, à condition qu'ils soient exposables: services, objets, prototypes, œuvres numériques, installation interactive, motion design, clip. Ils seront exposés aux Village des innovations du 12 au 15 juin.

 

Vous pouvez également organiser un événement partenaire (12 au 22 juin) sur tout le territoire de l'Ile-de-france.

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Graph Viz 101: Perceptual support of visualization - #dataviz #SNA #SNA_indatcom

Graph Viz 101: Perceptual support of visualization - #dataviz #SNA #SNA_indatcom | e-Xploration | Scoop.it
We are launching Graph Viz 101, a series of posts to teach the basics of graph visualization, written by Sébastien Heymann in collaboration with Bénédicte Le Grand of Université de Paris 1. This is our second post, please discuss it below!
luiy's insight:

Information visualization has been used to support social network analysis since the 1930s with the “sociogram” of J. Moreno (Moreno 1937), which is a graphic representation of social ties among a group of people. Despite the early beginning of network visuals, we had to wait until the 1990s and the democratization of computer graphics to see the development of interactive visualization software, which has made the interactive exploration of complex networks possible. Pajek (Batagelj 1998) is the most noticeable tool, as it provides both statistical algorithms and visual representations of social networks. Its methodological book entitled “Exploratory Social Network Analysis with Pajek” was published in 2005. The contribution of Information Visualization to science is stated in (Fekete 2008):

 

Information Visualization is meant at generating new insights and ideas that are the seeds of theories by using human perception as a very fast filter: if vision perceives some pattern, there might be a pattern in the data that reveals a structure. […] Therefore, it plays a special role in the sciences as an insight generating method.

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How educated are world leaders? | #dataviz

How educated are world leaders?  | #dataviz | e-Xploration | Scoop.it
In democracies, the driver of change is typically elections; in autocracies, it is typically biology. It is often said that autocratic leaders are therefore less likely to be educated.
luiy's insight:

In democracies1, the driver of change is typically elections; in autocracies, it is typically biology. It is often said that autocratic leaders are therefore less likely to be educated. Leaders' education level has been found to be a positive predictor of, among other things, economic growth2. Here's a look at the tenure of around 700 leaders3 sorted by political longevity in the world's autocratic and anocratic countries3, along with a selection from present-day democracies5, since 1950 or thereabouts. For each country, the leader who has ruled the longest, contiguously, is shown. The time in office (in years) may be off by a few months in either direction due to rounding. Hover over a bar for more details.

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Smartdrones - All news #drones

Smartdrones - All news #drones | e-Xploration | Scoop.it
Toute l'actualité des drones et du vol en immersion
luiy's insight:

- Surveillance

- Drones and regulations

- Control stations

- Drones and journalism

- Crafts

- Media Manufacturers

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#Mining the social web, again - O'Reilly Radar | #datascience #SNA_indatcom

#Mining the social web, again - O'Reilly Radar | #datascience #SNA_indatcom | e-Xploration | Scoop.it
When we first published Mining the Social Web, I thought it was one of the most important books I worked on that year. Now that we're publishing a second edition...
luiy's insight:

... With this new edition, Mining the Social Web is more important than ever.

 

While we’re seeing more and more cynicism about the value of data, and particularly “big data,” that cynicism isn’t shared by most people who actually work with data. Data has undoubtedly been overhyped and oversold, but the best way to arm yourself against the hype machine is to start working with data yourself, to find out what you can and can’t learn. And there’s no shortage of data around.

 

Everything we do leaves a cloud of data behind it: Twitter, Facebook, Google+ — to say nothing of the thousands of other social sites out there, such as Pinterest, Yelp, Foursquare, you name it. Google is doing a great job of mining your data for value. Why shouldn’t you?

There are few better ways to learn about mining social data than by starting with Twitter; Twitter is really a ready-made laboratory for the new data scientist. And this book is without a doubt the best and most thorough approach to mining Twitter data out there. But that’s only a starting point. We hear a lot in the press about sentiment analysis and mining unstructured text data; this book shows you how to do it. If you need to mine the data in web pages or email archives, this book shows you how. And if you want to understand how to people collaborate on projects,Mining the Social Web is the only place I’ve seen that analyzes GitHub data.

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Data privacy. Data brokers and I.. | #privacy #dataAwareness

Data privacy. Data brokers and I.. | #privacy #dataAwareness | e-Xploration | Scoop.it
Data privacy. Data brokers and I.., by Luiy: My data, my info, my privacy and the databrokers
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Hierarchical block structures and high-resolution model selection in large networks | #SNA #clusters

Hierarchical block structures and high-resolution model selection in large networks | #SNA #clusters | e-Xploration | Scoop.it

Discovering the large-scale topological features in empirical networks is a crucial tool in understanding how complex systems function. However most existing methods used to obtain the modular structure of networks suffer from serious problems, such as the resolution limit on the size of communities, where smaller but well-defined clusters are not detectable when the network becomes large. This phenomenon occurs for the very popular approach of modularity optimization, but also for more principled ones based on statistical inference and model selection. Here we construct a nested generative model which, through a complete description of the entire network hierarchy at multiple scales, is capable of avoiding this limitation, and enables the detection of modular structure at levels far beyond those possible by current approaches. Even with this increased resolution, the method is based on the principle of parsimony, and is capable of separating signal from noise, and thus will not lead to the identification of spurious modules even on sparse networks. Furthermore, it fully generalizes other approaches in that it is not restricted to purely assortative mixing patterns, directed or undirected graphs, and ad hoc hierarchical structures such as binary trees. Despite its general character, the approach is tractable, and can be combined with advanced techniques of community detection to yield an efficient algorithm which scales well for very large networks.


Via Claudia Mihai
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The Mathematical Shape of Big Science Data | #datascience #bigdata

The Mathematical Shape of Big Science Data | #datascience #bigdata | e-Xploration | Scoop.it
Scientific data sets are becoming more dynamic, requiring new mathematical techniques on par with the invention of calculus.

Via ukituki
luiy's insight:

DeDeo is not the only researcher grapping with these challenges. Across every discipline, data sets are getting bigger and more complex, whether one is dealing with medical records, genomic sequencing, neural networks in the brain, astrophysics, historical archives or social networks. Alessandro Vespignani, a physicist at Northeastern University who specializes in harnessing the power of social networking to model disease outbreaks, stock market behavior, collective social dynamics, and election outcomes, has collected many terabytes of data from social networks such as Twitter, nearly all of it raw and unstructured. “We didn’t define the conditions of the experiments, so we don’t know what we are capturing,” he said.

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New #Algorithm Can Spot the Bots in Your Twitter Feed | #dataviz

New #Algorithm Can Spot the Bots in Your Twitter Feed | #dataviz | e-Xploration | Scoop.it
Researchers have created an algorithm that can tell—with 85 percent accuracy—whether a Twitter account is home to a bot or (worse) a corporate shill.

Via Pierre Levy
luiy's insight:

You know Twitter spam when you see it—but wouldn’t it be nice if you didn’t have to see it?

 

Unfortunately, email-style filters, which analyze message contents, are of little help. Due to the rigors of 140-character communication, even legitimate tweets tend to read like Nigerian phishing scams, while the hucksters often hide their pitches in links. So Twitter simply puts the onus on users to report offending accounts.

 

But a fascinating recent study from Imperial College London suggests a new approach. Borrowing some tricks from computational neuroscience, coauthors Gabriela Tavares and Aldo Faisal have come up with an algorithm that can tell—with 85 percent accuracy—whether a Twitter account is home to a bot or (worse) a corporate shill instead of a regular person.

 

It’s all in the timing. By analyzing the timestamps on 165,000 tweets, the researchers found that these three user types—individuals, companies, and robots—have very distinct activity patterns. Think of it as temporal fingerprinting. The approach could eventually be used to create more effective filters for all kinds of social networks.

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The #sierpinski triangle page to end most sierpinski triangle pages | #patterns #fractals #dataviz

The #sierpinski triangle page to end most sierpinski triangle pages | #patterns #fractals #dataviz | e-Xploration | Scoop.it

Via Claudia Mihai
luiy's insight:
Constructing the Sierpinski triangle

Throughout my years playing around with fractals, the Sierpinski triangle has been a consistent staple. The triangle is named after Wacław Sierpiński and as fractals are wont the pattern appears in many places, so there are many different ways of constructing the triangle on a computer.

All of the methods are fundamentally iterative. The most obvious method is probably the triangle-in-triangle approach. We start with one triangle, and at every step we replace each triangle with 3 subtriangles:

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Information #Visualization for Large-Scale Data #Workflows | #SNA_indatcom

The ability to instrument and interrogate data as it moves through a processing pipeline is fundamental to effective machine learning at scale. Applied in this
luiy's insight:

..... Applied in this capacity, information visualization technologies drive product innovation, shorten iteration cycles, reduce uncertainty, and ultimately improve the performance of predictive models. It can be challenging, however, to understand where in a workflow to employ data visualization, and, once committed to doing so, developing revealing visualizations that suggest clear next steps can be similarly daunting. 

In this talk we’ll describe the role that information visualization technologies play in the LinkedIn data science ecosystem, and explore best practices for understanding the structure of large-scale data in a production environment. From hypothesis generation and feature development to model evaluation and tooling, visualization is at the heart of LinkedIn’s machine learning workflows, enabling our data scientists to reason and communicate more effectively. Broken down into clear, structured insights based on proven workflow patterns, this talk will help you understand how to apply information visualization to the analytical challenges you encounter every day.

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Tweet Archivist Desktop | #dataviz #socialmedia

Tweet Archivist Desktop | #dataviz #socialmedia | e-Xploration | Scoop.it
Tweet Archivist, an desktop application tool to archive, analyze, visualize, save and export tweets.
luiy's insight:
Tweet Archivist Desktop is a Windows application that helps you archive tweets for later data-mining and analysis. Start a search with Tweet Archivist and it will get as many results as it can. Then, leave Tweet Archivist running and it will poll Twitter for that search as frequently as once every five minutes.
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Social Network Analysis & an Introduction to Tools | #dataviz #SNA_indatcom #SNA

This presentation covers the basics of network analysis and then goes into the different types of tool that support analyzing networks.

 
Via Stephen Dale, Kenneth Mikkelsen
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Stephen Dale's curator insight, July 21, 2013 7:41 AM

A great introduction to the power and benefits of SNA. Some useful pointers to SNA tools as well.

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Misère de l’humanité #numérique - La Vie des idées - #controverses

Misère de l’humanité #numérique - La Vie des idées - #controverses | e-Xploration | Scoop.it
La compréhension du monde et sa transformation peuvent-elles se réduire à de simples questions de programmation ? Alors que vient de paraître son (...)
luiy's insight:

Ce monde numérique, où nous construisons une part croissante de notre existence, est façonné par à peine plus d’un millier de scientifiques et d’ingénieurs en informatique, dont la plupart œuvrent dans la Silicon Valley. Ils sont à l’origine, entre autres, du langage HTML, de l’ordinateur personnel, du iPhone, des principaux logiciels que nous utilisons chaque jour et des sites internet les plus visités au monde.

 

Depuis trente-cinq ans, Jaron Lanier est l’un d’entre eux, même s’il ne leur ressemble pas. D’un côté, il est le père du concept de « réalité virtuelle », il a été le colocataire du fondateur du mouvement pour le logiciel libre, Richard Stallman, et il est membre du très influent Global Business Network. D’un autre côté, il n’est partisan ni de l’open source, ni de la gratuité de l’information, ni du web 2.0 ; il n’est pas en admiration devant les progrès de l’intelligence artificielle ; il ne lit pas avidement boingboing.net ou le magazine Wired (bien qu’il lui arrive d’écrire dans le second) ; et surtout, il exècre la doxa qui anime cette petite communauté et qui est en train de devenir, à ses yeux, le sens commun des sociétés numériques.

 

Cette idéologie repose tout entière sur un axiome : la vie est un système d’information. Selon cette perspective, une galaxie, l’océan, un être humain, une pensée ou une émotions sont des assemblages d’informations, des sortes de bases de données qu’un algorithme doit pouvoir rendre intelligibles et manipulables. Par un étrange retournement, en même temps que les ingénieurs qu’égratigne Lanier réduisent les individus à un rôle d’émetteurs d’informations et de calculateurs, ils font des informations de véritables personnes douées de vie et de conscience. La liberté dont ils parlent sans cesse s’applique à ces informations, et non aux individus dont elles maillent les existences. Tel est le cœur de la critique que Lanier adresse à la Silicon Valley.

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Vizrt and Lockheed Martin Work on #BigData #Visualization Tools | #dataviz

Vizrt and Lockheed Martin Work on #BigData #Visualization Tools | #dataviz | e-Xploration | Scoop.it
Broadcast news products projects for digital content creators in Film, Broadcast, Video, VFX, visual effects, Animation, Web,Games and Mobile
luiy's insight:

Lockheed Martin is a security and aerospace company engaged in research for system design, development, manufacture and integration. The first large-scale big data visualization implemented from their Vizrt collaboration is MED-E, a dashboard specializing in Energy, designed to graphically illustrate layers of high-volume information about the status of energy resources, distribution and supply. For example, WindOptimizer is a MED-E suite of cloud-based services distributing real-time sensor data, including weather forecast prediction and wind energy supplies, so that wind farms, power companies and other energy companies can manage wind resources more cost-efficiently.  

 

Vizrt R&D teams in Europe and designers and support in the United States worked with Lockheed Martin to design a system that produces data visualization to a high standard but at the same time remains flexible in its application so that several different industries can use the product, customising it for their specific purposes.  

For Vizrt, developing the MED-E platform meant pushing Viz Artist and Viz Engine as a live data harvester and interactive visualization platform. Petter Ole Jakobsen, Vizrt’s CTO in Bergen, Norway said, “For MED-E, we’re taking multiple silos of data sources and coalescing them into one, scalable, big picture visualization representing all these different data layers. We are also doing this live when live data is available.” 

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#FreeBook : "Network Science Book" | #dataviz #BarabasiLab

#FreeBook : "Network Science Book" | #dataviz #BarabasiLab | e-Xploration | Scoop.it
The power of network science, the beauty of network visualization.
luiy's insight:
The power of network science, the beauty of network visualization.

Network Science Book Project aims to produce an interactive textbook for network science. It is a work in progress, as we add chapters as they are finalized. Currently you will find Chapter 1-6, and we hope to have ten chapters by the end of the year. It is freely available under the Creative Commons licence for iPad and in pdf, together with the slides to teach the material. Feel free to offerfeedback and follow its development on Facebook, Twitter or by signining up to our mailing list, so that we can notify you of new chapters and developments.

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Edgar Morin, une voie pour éviter le désastre annoncé | #socialchange

Edgar Morin, une voie pour éviter le désastre annoncé | #socialchange | e-Xploration | Scoop.it
Entretien avec l'ancien résistant et philosophe qui, dans son livre "La Voie", dresse un constat sévère des maux contemporains.
luiy's insight:

Vous défendez dans « La Voie » la démocratie participative, un concept de Royal.

 

Elle avait raison. Il y a des exemples au Brésil où la population examine certains budgets... Il doit y avoir je crois comme complément à la démocratie parlementaire et institutionnelle une démocratie de base qui puisse contrôler, voire décider de certains problèmes comme la construction d’un bout d’autoroute, l’installation d’une usine...

Je suis favorable à la démocratie participative mais je sais que ce n’est pas une solution magique. Le risque est que les populations les plus concernées en soit absentes, les vieux, les femmes, les jeunes, les immigrés...

 

Il y a aussi le risque que ces assemblées soient noyautées par des partis. Cette manie des petits partis trotskistes de toujours noyauter. Ils croient bien faire et en réalité, ils détruisent tout ! Voyez l’altermondialisme.

 

Souvent aussi, ce sont les forts en gueule qui jouent les rôles les plus importants et beaucoup se taisent. Il y a toute une éducation à faire sur la démocratie participative.

 

Si on amorce la pompe au renouveau citoyen, les choses peuvent se développer. Il faut créer des instituts où l’on enseigne aux citoyens les grands problèmes politiques. Comme il y a un dessèchement de la démocratie, la régénération de la démocratie compte.

 

Pourtant, la plus grande difficulté, c’est le désenchantement. Les vieilles générations ont cru à la révolution, au communisme, à la société dite industrielle, à la prospérité, à la fin des crises. Raymond Aron disait : « La société industrielle ferait la moins mauvaise société possible. » Il y avait des espoirs, le socialisme arabe, les révolutions... Tous ces espoirs se sont effondrés.

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Thoughts on “Collective Intelligence”

Thoughts on “Collective Intelligence” | e-Xploration | Scoop.it

Via Pierre Levy
luiy's insight:

To support these claims, Lévy relies mainly on two theoretical models: a “cartography” – in the sense of a kind of Carte du Tendre (p. 149) – of anthropological spaces, and an analysis of crucial technological evolutions.

 

The model of anthropological spaces (summarized in Figure 1 below) comprises four interdependent and coexisting layers: autonomous (p. 243), metamorphic (p. 170), living (p. 227) worlds of signification (p. 149) structured by affective forces (p. 143). Such spaces of signification implicate their own axiology and system of values or measurement (p. 144; cf. Serres, 1972). Each new space subsumes and subordinates the previous ones without eradicating or assimilating them (p. 236). Even in our cyberized society, the “great chaosmic earth” (p. 132) of totems and rites is still with us; indeed, the fabric of our humanity is irrevocably woven with the threads of dreams and myths. Nonetheless, this mutual inter-penetration of anthropological spaces must be understood as a chaotic, complex process (p. 150).

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María Dolores Díaz Noguera's curator insight, January 14, 4:50 AM

Great one.

Miguel Angel Perez Alvarez's curator insight, January 14, 2:14 PM

Inteligencia colectiva, evolución

Deb Nystrom, REVELN's curator insight, January 31, 11:18 PM

Cyberspace big thoughts, and perhaps indicating a fusion or an evolution of knowledge management in a group space.

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Stop Watching Us: The Video | #PRISM #surveillance #privacy

StopWatching.us is a coalition of more than 100 public advocacy organizations and companies from across the political spectrum. Join the movement at https://...
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ANONYMOUS IN CONTEXT: THE POLITICS AND POWER BEHIND THE MASK | #socialchange


Anonymous is a banner used by individuals and various ad hoc and stable groups unfurling political operations across the globe from Brazil to the Philippines from the Dominican Republic to India. By 2012 Anonymous became multitudinous, prolific, and unpredictable.

Gabriella Coleman examines the various factors that secure their dynamic presence, mutability, plurality and flexibility and their contemporary geopolitical power including its ability to land media attention, its bold and recognizable aesthetics, its participatory openness, and the misinformation that surrounds it. One feature stands out in this mix: Anonymous' unpredictability. It is difficult to know when or why it will strike, when a new node will appear, whether a campaign will be successful, and how Anonymous might change direction or tactics during the course of an operation. Anonymous, borne through raucous experimentation, still values errantry and play, their unpredictability a central ingredient to their thunderous rise and success.


Via John Postill
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John Postill's curator insight, October 26, 2013 11:06 AM

Antropología Digital ‏@antroposdigital now

ANONYMOUS IN CONTEXT: THE POLITICS AND POWER BEHIND THE MASK Gabriella Coleman

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Community Structure and Multi-Modal Oscillations in #Complex Networks => #SNA #datascience

Community Structure and Multi-Modal Oscillations in #Complex Networks => #SNA #datascience | e-Xploration | Scoop.it
PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.

Via ukituki
luiy's insight:

Methods

 

In order to rigorously establish the relationship between network structure and dynamics, we require a model system that is broadly applicable, but which supports a wide range of dynamical behaviours. We also need to be able to measure the global network dynamics in a way that readily admits analysis. The well-established Kuramoto model [5], [42], [43] meets all of these requirements and is widely used in related work [44]–[47].

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ukituki's curator insight, October 16, 2013 6:50 AM

In many types of network, the relationship between structure and function is of great significance. We are particularly interested in community structures, which arise in a wide variety of domains. We apply a simple oscillator model to networks with community structures and show that waves of regular oscillation are caused by synchronised clusters of nodes. Moreover, we show that such global oscillations may arise as a direct result of network topology.


We also observe that additional modes of oscillation (as detected through frequency analysis) occur in networks with additional levels of topological hierarchy and that such modes may be directly related to network structure. We apply the method in two specific domains (metabolic networks and metropolitan transport) demonstrating the robustness of our results when applied to real world systems.


We conclude that (where the distribution of oscillator frequencies and the interactions between them are known to be unimodal) our observations may be applicable to the detection of underlying community structure in networks, shedding further light on the general relationship between structure and function in complex systems.

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#FRACTALS : #logarithmic chaos game on vertices of stellated 5-pyramid | #patterns

see http://www.oftenpaper.net/flipbook-fadeleafanimation.htm for the source and http://www.oftenpaper.net/sierpinski.htm for the context
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Interactive timeline of #PRISM scandal with a dynamic network graph | #dataviz #SNA_indatcom

Interactive timeline of #PRISM scandal with a dynamic network graph | #dataviz #SNA_indatcom | e-Xploration | Scoop.it
This is an interactive timeline of events about the Prism scandal, chronicled by selected media in online news articles, giving a summarized view of events as they unfolded.
luiy's insight:

It’s intended as a parody of a NSA software to track people and analyze their metadata. It consists of these parts:


•the chronological order of articles, visualized as a timeline,
•a network of people, places and organizations that appear in the articles,
•geographic information that the articles refer to, and
•a bar graph showing wordcounts of interesting words, associated with the main theme.


Note: it won't work in IE.

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Twitter Archiving Google Spreadsheet TAGS v5 MASHe | #dataviz #extracting #SNA_indatcom

Twitter Archiving Google Spreadsheet TAGS v5 MASHe | #dataviz #extracting #SNA_indatcom | e-Xploration | Scoop.it
For a couple of years I've been sharing a Google Sheet template for archiving searches from Twitter. In September 2012 Twitter announced the release of a new version of their API (the spreadsheet uses this to request data from Twitter).
luiy's insight:
Twitter Archiving Google Spreadsheet TAGS v5

For a couple of years now to support my research in Twitter community analysis/visualisation I’ve been developing my Twitter Archiving Google Spreadsheet (TAGS). To allow other to explore the possibilities of data generated by Twitter I’ve released copies of this template to the community.

 

In September 2012 Twitter announced the release of a new version of their API (the spreadsheet uses this to request data from Twitter). Around the same time Twitter also announced that the old version of their API would be switched off in March 2013. This has required some modification of TAGS to work with the new API. The biggest change for TAGS is that all requests now need authenticated access.

So here it is:

 

*** Twitter Archive Google Spreadsheet – TAGS v5.0 ***


[If the first link doesn't work try Opening this Spreadsheet and File > Make a copy]

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