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antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
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The Making of “History of #Rock in 100 Songs” | #dataviz #sna #gephi

The Making of “History of #Rock in 100 Songs” | #dataviz #sna #gephi | e-Xploration | Scoop.it
[This is a guest post by Susie Liu*, about the visualization project “History of Rock in 100 Songs”]
 
 
Figure 1. History of Rock in 100 Songs screenshot, live site here
 
The world of Rock ‘n’ Roll has always been very confusing to me.
luiy's insight:

When I found the Guardian’s list of 100 songs representative of rock music, I was inspired to use skills from my day job, creating data visualizations, to learn more about Rock ‘n’ Roll. With John as a domain expert to sanity check my work and a quick Google search of music APIs, we were ready to begin.

 

The original Guardian dataset only had the song title, artist, and release year of the song. I looked up more band information, such as its members and lifetime, using Last.fm and more song information, such as length and energy, using Echo Nest.

 

Exploring the data began with asking questions. I started simple. How do these songs appear over time? How do the timelines of the bands overlap with each other? I begin all of my visual brainstorming on paper like in Figure 2. It allows me to quickly see ideas. Plus, there is no hesitation to nix bad ideas because I’ve only invested a few minutes in the concept.

 

- See more at: http://visualoop.com/21745/the-making-of-history-of-rock-in-100-songs#sthash.9JUvGyoV.dpuf

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Lincoln #Logarithms: Finding Meaning in Sermons | #dataviz #sna #DH

Lincoln #Logarithms: Finding Meaning in Sermons | #dataviz #sna #DH | e-Xploration | Scoop.it
luiy's insight:

The content and the tools


We explored the power and possibility of four digital tools—MALLET, Voyant, Paper Machines, andViewshare.  MALLET, Paper Machines, and Voyant all examine text.  They show how words are arranged in texts, their frequency, and their proximity. Voyant and Paper Machines also allow users to make visualizations of word patterns. Viewshare allows users to create timelines, maps, and charts of bodies of material. In this project, we wanted to experiment with understanding what these tools, which are in part created to reveal, could and could not show us in a small, but rich corpus.  What we have produced is an exploration of the possibilities and the constraints of these tools as applied to this collection.

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Data Visualization #Design by Etan Lightstone at FutureStack13 | #dataviz #workflow

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Data visualizations have become a first class citizen of information dissemination on the web, and a powerful tool when used effectively in product user interfaces . With technologies like D3, we are able to provide a great deal of interactivity to these visualizations, and with almost unlimited possibilities. This talk will be focusing on how to design effective data visualizations:

* Design process for data viz
* Visual design patterns to follow
* Using the right charts
* Data mining, and cutting through the noise of very large data sets

 

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Which social media network type is your topic? Which did you want it to be? | #SNA #datascience

Which social media network type is your topic?  Which did you want it to be? | #SNA #datascience | e-Xploration | Scoop.it
There are at least six different types of social media network structures present in systems like Twitter and other services in which people are able to reply to one another. Each of the six patter...
luiy's insight:

This table describes each of the six patterns in terms of the difference between that pattern and the other five patterns.

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The Anatomy of a Forgotten Social Network | #SNA #Tumblr

The Anatomy of a Forgotten Social Network | #SNA #Tumblr | e-Xploration | Scoop.it
While network scientists have been poring over data from Twitter and Facebook, they’ve forgotten about Tumblr. Now they’ve begun to ask how this network differs from the rest.
luiy's insight:

The study of social networks has gripped computer scientists in recent years. In particular, researchers have focused on a few of the biggest networks that have made their data available, such as some mobile phone networks, Wikipedia and Twitter.


But in the rush, one network has been more or less ignored by researchers: Tumblr, a microblogging platform similar to Twitter. So an interesting question is how the network associated with Tumblr is different from the Twitter network.


Today we get an answer thanks to the work of Yi Chang and pals at Yahoo Labs in Sunnyvale. These guys point out that relatively little is known about Tumblr compared to other networks like Twitter and set out to change this.


The basic statistics are straightforward. Tumblr is a microblogging service with about 160 million users who together have published over 70 billion posts.


The most significant difference between Tumblr and its bigger cousin, Twitter, is that there is no limit to the size of the posts that users can create. By contrast, Twitter imposes the famous 140-character limit on all of its posts. Tumblr also supports multimedia posts, such as images, audio, and video.

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#Truthy : Information diffusion research | #political #memes #patterns #SNA

#Truthy : Information diffusion research | #political #memes #patterns #SNA | e-Xploration | Scoop.it
luiy's insight:

Information diffusion research at Indiana University

 

Truthy is a research project that helps you understand how communication spreads on Twitter. 

 

We currently focus on tweets about politics, social movements and news.

 

 

Political Topics

Interactive visualizations of U.S. political conversation on Twitter :

 

- How does sentiment change over time in response to political events?

- What is most popular over time?

- Who are the most influential users?

- How does information spread in the social network?

 

 

Sentiment Timeline

- How does sentiment change over time in response to political events?

 

 

Gallery Descriptions of interesting memes:  http://truthy.indiana.edu/gallery

 

 

Meme Patterns:

What other memes are related to this one?  http://truthy.indiana.edu/memedetail?id=783&resmin=45&theme_id=4

 

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Intro materials: network analysis software (UCINET, NodeXL, Gephi, Statnet, ERGM, RSiena) | #datascience #SNA

Intro materials: network analysis software (UCINET, NodeXL, Gephi, Statnet, ERGM, RSiena) | #datascience #SNA | e-Xploration | Scoop.it
Introductory materials, handouts and R scripts for network analysis and visualization.

 

In the fall of 2012, I got to design & lead the weekly labs for a network seminar at USC. I also worked on the methods portion of the syllabus for the class. COMM 645: Communication Networks is a PhD-level course taught by Peter Monge. The labs cover a range of network tools – from the classic UCINETprogram through NodeXL and Gephi, to R introduction, Statnet, exponential random graph and actor-based modeling. Since the handouts & script examples may be useful for people outside the course, I’m sharing them here.


Via Pierre Levy
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Rescooped by luiy from Social Network Analysis #sna
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Social networks for managers | #SNA #KM #ONA

Revision of Previous Show on SNA and Introduction to Tools The Language of Networks Introduction to Social Network Analysis/ Cases Tools for Analyzing social...

Via june holley, ukituki
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june holley's curator insight, March 5, 2014 11:20 AM

Lots in here about social network mapping and analysis.

Liz Rykert's curator insight, March 6, 2014 12:58 PM

Thanks for this one June!

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Thoughts on #SNA and online #learning | #intelligencecollective

Thoughts on #SNA and online #learning | #intelligencecollective | e-Xploration | Scoop.it
Following the previous post... The structural paradigm of  Social Network Analysis (SNA) with its constitutive theory and methods, began to emerge around the 1930s, applied and influenced by a broa...

Via Dr. Susan Bainbridge, Marinella De Simone
luiy's insight:

The connections within nodes in a network facilitate exchange of “resources”  which can be influenced by the quantity and quality of the linkages and interactions. Looking at online educational networks through a SNA lens is a way to establish wether the ways in which individuals connect with a particular environment may influence their access to information and knowledge. As Rita Kop states “the Web is portrayed as a democratic network on which peer to peer interaction might lead to a creative explosion and participative culture of activity” (Kop, 2012 p3) but how is this potential being exploited in education? What are the processes beyond this interaction and how can they be used to facilitate students access to information, knowledge and ideas?

 

The potential of social media in forming networks, extending students knowledge and translating this into academic achievement is impacted by a multitude of elements such as individuals’ attitudes (Morrison, 2002), University environment and socialisation processes (Yu et al., 2010). Other mechanisms influencing this process may be the particular educational practices and experiences, the success of connections, the dynamics in which participants negotiate the structure of the network and exchange practices and many others which can not be controlled.

 

This analysis can be enriched by Bordieau’s concept of “social capital”, which introduces a set of dynamics between the social dimension, the identity dimension (habitus) and the individual’s practice. In this system of reciprocal influences it is interesting to look at the transformation processes and effects of elements such as “weak ties”, “brokers”, “latent connections” and “structural holes” in the information flow within a network.

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Milena Bobeva's curator insight, March 1, 2014 4:10 AM

Social Network Analysis should be a  paradigm for researching, designing, and evaluating not only online learning, but  the wider phenomenon of Education 3.0

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Multi-layered Social Networks | #SNA #Multidimensional

luiy's insight:

Synonyms


- Multi-layered Social Networks,
- Layered social network,
- Multi-relational social network,
- Multidimensional social network,
- Multiplex social network

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A crowd-funded project to bring graph analytics to justice | #SNA via @Linkurious

A crowd-funded project to bring graph analytics to justice | #SNA via @Linkurious | e-Xploration | Scoop.it

A crowd-funded project from Stanford aims to use graph technologies to better understand international justice. Like Linkurious, you can support this research project and help scientists. You can help apply graph analytics to shed light on the way justice works The justice system is not the most transparent and data friendly domain. Quite the contrary. …

luiy's insight:
You can help apply graph analytics to shed light on the way justice works

The justice system is not the most transparent and data friendly domain. Quite the contrary. That’s why it’s so exciting to see that researchers like Sergio Puig from Stanford and Enric Torrents from MIT are trying to bring data analysis techniques to legal studies. Can social network analysis and graphs help improve justice systems?

 
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The Five Graphs of Love | #Neo4j #SNA #algorithms

The iDating industry cares about interactions and connections. Those two concepts are closely linked. If someone has a connection to another person, through a shared…
luiy's insight:

Dating sites and apps worldwide have begun to use graph databases to achieve competitive gain. Neo4j provides thousand-fold performance improvements and massive agility benefits over relational databases, enabling new levels of performance and insight. Join us for a webinar, presented by Amanda Laucher, that discusses the five graphs of love, and how companies like eHarmony, Hinge and AreYouInterested.com, are now using graph algorithms to create more interactions and connections.

 

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The Surprising Science Behind How Super Connectors Scale Their Networks | #SNA #CommunityBuilding

The Surprising Science Behind How Super Connectors Scale Their Networks | #SNA #CommunityBuilding | e-Xploration | Scoop.it

How one of the world’s top super connectors uses scientific principles of social network analysis to dramatically scale the size and depth of his network without increasing the time spent. 

 


Via Kenneth Mikkelsen
luiy's insight:

The basic idea behind Metcalfe’s Law is that the ‘potential value’ of a network increases exponentially as you add new interconnected nodes. In the context of relationship building, this means that as you build relationships that are connected to each other, the value of the network increases exponentially.

 

In the parlance of social network analysis, density is the percentage of people in your network connected to each other that could be. By increasing density, new things spread more quickly through it.

What sorts of new things?

 

New research by professors Nicholas Christakis and James Fowler, authors of Connected: The Surprising Power of Social Networks And How They Shape Our Lives, shows that a surprisingly large number of things are spread through networks such as violence, money, happiness, germs, obesity, gossip, ideas, norms, and behaviors.

In other words, as we increase the density of our networks based on mutual support, we dramatically increase the rate at which its participants learn from each other and deepen their relationships.

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Four Ways of Looking at Twitter | #dataviz #SNA

Four Ways of Looking at Twitter | #dataviz #SNA | e-Xploration | Scoop.it
Data visualization is cool. It's also becoming ever more useful, as the vibrant online community of data visualizers (programmers, designers, artists, and statisticians — sometimes all in one...
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visone : analysis & visualization of social networks | #SNA #datascience

visone : analysis & visualization of social networks | #SNA #datascience | e-Xploration | Scoop.it
luiy's insight:

On the applications page in the visone wiki we list research projects in which visone has been applied as well as datasets on which the usage of visone can be illustrated. We are planning to release a demonstration video soon, too. In the meanwhile you might want to jump directly into basic and advanced tutorials, that focus on differnet aspects of the software.

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The French #Incubators Network | #SNA #dataviz

The French #Incubators Network | #SNA #dataviz | e-Xploration | Scoop.it
The French Incubators network: startups mapping, venture, social network connections
luiy's insight:

This approach give us the chance to catch a glimpse of the network density. It’s really interesting to notice we have a complete list of organizations, individuals, companies having different roles in the start-ups landscape: incubator, startups accelerator, venture, entrepreneur’s fund, mentoring, association of entrepreneurs, magazine, blogs. This unveils the momentum in France around startups development and the will to setup various structures to promote entrepreneurship.

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K-CORE DECOMPOSITION OF INTERNET GRAPHS: HIERARCHIES, SELF-SIMILARITY AND MEASUREMENT BIASES | #datascience #SNA

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Data Mining #Algorithms In R/Clustering/K-Cores | #datascience #SNA

Data Mining #Algorithms In R/Clustering/K-Cores | #datascience #SNA | e-Xploration | Scoop.it
luiy's insight:
Cores

The notion of core is presented in Butts (2010) as following:

 

Let G = (V, E) be a graph, and let f (v, S, G) for v ∈ V, S ⊆ V be a real-valued vertex property function (in the language of Batagelj and Zaversnik). Then some set H ⊆ V is a generalized k-core for f if H is a maximal set such that f (v, H, G) ≥ k for all v ∈ H. Typically, f is chosen to be a degree measure with respect to S (e.g., the number of ties to vertices in S). In this case, the resulting k-cores have the intuitive property of being maximal sets such that every set member is tied (in the appropriate manner) to at least k others within the set.

 

Degree-based k-cores are a simple tool for identifying well-connected structures within large graphs. Let the core number of vertex v be the value of the highest-value core containing v. Then, intuitively, vertices with high core numbers belong to relatively well-connected sets (in the sense of sets with high minimum internal degree). It is important to note that, while a given k-core need not be connected, it is composed of subsets which are themselves well-connected; thus, the k-cores can be thought of as unions of relatively cohesive subgroups.

 

As k-cores are nested, it is also natural to think of each k-core as representing a “slice” through a hypothetical “cohesion surface” on G. (Indeed, k-cores are often visualized in exactly this manner.)

The kcores function produces degree-based k-cores, for various degree measures (with or without edge values). The return value is the vector of core numbers for V , based on the selected degree measure. Missing (i.e., NA) edge are removed for purposes of the degree calculation.

 
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An Interactive Introduction to Network Analysis and Representation | #SNA #tools

An Interactive Introduction to Network Analysis and Representation | #SNA #tools | e-Xploration | Scoop.it
luiy's insight:

This interactive application is designed to provide an overview of various network analysis principles used for analysis and representation. It also provides a few examples of untraditional networks used in digital humanities scholarship. Finally, along with the various methods described interactively here are links to related scholarship.

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A Brief Primer on Human Social Networks, or How to Keep $16 Billion In Your Pocket | #identity #socialmedia #dumbar

A Brief Primer on Human Social Networks, or How to Keep $16 Billion In Your Pocket | #identity #socialmedia #dumbar | e-Xploration | Scoop.it
Over at The New York Times, Jenna Wortham wonders whether Facebook’s acquisition of Whatsapp points to a resurgence of small social…
luiy's insight:

Facebook’s key problem for many people has been what academics sometimes call “context-collapse,” which is the sense that Facebook sometimes feels like an extended Thanksgiving dinner where everyone you have ever known is at the table. This is an identity-constraining environment as it’s hard to know how to address such a large crowd at the same time. People have been grappling with this for a long time and have come up with a variety of solutions, including fleeing to Twitter & Instagram and, yes, Whatsapp.

 

Social scientists have long been trying to communicate this to technology companies: it is normal, natural and healthy to have different communication needs at different levels of one’s social network. One wonders if, early on, Mark Zuckerberg had listened to social scientists rather than declaring “having two identities for yourself is an example of a lack of integrity”, would he now have $16 billion more in his pocket?

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Datavisualization Selected #Tools | #dataviz #SNA

Datavisualization Selected #Tools | #dataviz #SNA | e-Xploration | Scoop.it
Datavisualization.ch Selected Tools is a collection of tools that we, the people behind Datavisualization.ch, work with on a daily basis and recommend warmly.
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¿Bots rezando por Venezuela? Un análisis de #PrayForVenezuela | #SNA #controverses via @AlbertoEscorcia

¿Bots rezando por Venezuela? Un análisis de #PrayForVenezuela | #SNA #controverses via @AlbertoEscorcia | e-Xploration | Scoop.it
Esto es un ejercicio para tratar de entender las recientes protestas ocurridas en Venezuela desde los pasados 12 y 13 de Febrero donde a través de etiquetas y tendencias de Twitter han llegado masivos reportes de violencia, represión de protestas, de supuesta censura e incluso las afirmaciones y el llamado …
luiy's insight:

Entiéndase esto pues como una interpretación, no como una afirmación  y menos como una postura. Solo en Venezuela los venezolanos saben qué ocurre, en el mundo tratamos de entender a la distancia y que sirva este esfuerzo par abonar al entendimiento porque existen muchas preguntas.

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#Gephi plugin for depth-first searching for closed cycles on graphs | #SNA

#Gephi plugin for depth-first searching for closed cycles on graphs | #SNA | e-Xploration | Scoop.it
This free plugin for gephi traverses the active graph searching for closed walks, cycles and cliques. It can be used on both directed and undirected graphs.
luiy's insight:

This Gephi plugin traverses the active graph searching for closed walks, cycles and cliques. The search is made using the popular depth-first order search algorithm, using a standard single stack implementation made popular by Robert Sedgewick. Although this is very common in graph searching, there wasn’t a plugin for Gephi performing just this simple task and no more in a efficient way. Some features:

 

The plugin can be used on both directed and undirected graphs. In the first case, close walks (cycles) are hunted, while cliques in the latter; Reports include a distribution of the founded cycles by size; No use of external libraries: just 18k for the whole package; Asynchronous and interruptible task; Written in a pure OOP flavour, using Gephi APIs.
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