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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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Connecting Dream Networks Across Cultures | #SNA #symbols

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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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StarGate: A Unied, #Interactive Visualizatio of Software Projects | #dataviz #SNA

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The ‘Fragile Five’ will not create a new global #economic #crisis | #SNA

The ‘Fragile Five’ will not create a new global #economic #crisis | #SNA | e-Xploration | Scoop.it
Fears that the 'Fragile Five' will provoke a new global financial crisis are overblown. The cycle of economic instability is driven by the political economy of the US, not peripheral countries.
luiy's insight:

We should think of the global financial system as a network of financial relationships, in which some countries are heavily connected to everyone else, while others are only weakly connected.  For example, if you look at cross-border portfolio assets, there are enormous differences between the center and the periphery. As the figure above (based on IMF cross-border portfolio investment data for the end of 2012) illustrates, the U.S. is right at the center of the network, attracting investment in large amounts from almost every country in the system. Most countries, including the Fragile Five, sit on the periphery, attracting limited investment from a very small number of countries. The UK occupies an intermediate position in the network—not quite as central as the U.S., but certainly not peripheral either. Large EU countries are in turn less central than the UK, but less peripheral than the remaining countries. Though America’s centrality in the global financial system is widely understood, people have thought little about how this structure affects the system as a whole.

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#SNA applications with #R: Statnet, ergm, igraph, RSiena, networksis, latentnet | #datascience

#SNA applications with #R: Statnet, ergm, igraph, RSiena, networksis, latentnet | #datascience | e-Xploration | Scoop.it
luiy's insight:

1. ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks.

An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGM). "ergm" is a part of the "statnet" suite of packages for network analysis.

 

http://cran.r-project.org/web/packages/ergm/index.html

 

 

2. igraph: Network analysis and visualization.

Routines for simple graphs and network analysis. igraph can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality indices and much more.

 

http://cran.r-project.org/web/packages/igraph/index.html

 

 

3. RSiena: Siena - Simulation Investigation for Empirical Network Analysis.

Fits models to longitudinal network data.

 

http://cran.r-project.org/web/packages/RSiena/index.html

 

 

4. networksis: Simulate bipartite graphs with fixed marginals through sequential importance sampling

Tools to simulate bipartite networks/graphs with the degrees of the nodes fixed and specified. 'networksis' is part of the 'statnet' suite of packages for network analysis.

 

http://cran.r-project.org/web/packages/networksis/index.html

 

 

5. latentnet: Latent position and cluster models for statistical networks.

A package to fit and simulate latent position and cluster models for statistical networks.

 

http://cran.r-project.org/web/packages/latentnet/index.html

 

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Visualising Related Entries in #Wikipedia Using #Gephi | #tutorial #SNA

Visualising Related Entries in #Wikipedia Using #Gephi | #tutorial #SNA | e-Xploration | Scoop.it
Sometime last week, @mediaczar tipped me off to a neat recipe on the wonderfully named Drunks&Lampposts blog, Graphing the history of philosophy, that uses Gephi to map an influence network in ...
luiy's insight:

To get DBpedia data into Gephi, we need to do three things:

 

- tell the importer where to find the data by giving it a URL (the “Driver” configuration setting);


- tell the importer what data we want to get back, by specifying what is essentially a database query (the “Request” configuration setting);


- tell Gephi how to create the network we want to visualise from the data returned from DBpedia (in the context of the “Request” configuration).

 

Fortunately, we don’t have to work out how to do this from scratch – from the Semantic Web ImportConfiguration panel, configure the importer by setting the configuration to DBPediaMovies.

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#Sigmajs 1.0, the next-gen graph drawing lib for the Web, is out! | #dataviz #SNA

#Sigmajs 1.0, the next-gen graph drawing lib for the Web, is out! | #dataviz #SNA | e-Xploration | Scoop.it

At Linkurious our mission is to deliver enterprise-level applications for graph visualization and exploration. We naturally integrate world-class open source technologies in our products, like Neo4j, ElasticSearch and Node.js on the backend. On our Web frontend, graphs are rendered using our fork of Sigma.js, the most efficient graph visualization library on the market.

luiy's insight:

Today the new release of Sigma.js is out! Backed by our friends at Sciences-Po Medialab in Paris, the project lead by Alexis Jacomy “is a JavaScript librarydedicated to graph drawing. It makes easy to publish networks on Web pages, and allows developers to integrate network exploration in rich Web applications.” Among the many improvements, this new version comes with:

Plugin systemRenderer-agnostic system that works with SVG/Canvas/WebGLUnit testsTouch support
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Machine learning and social network analysis applied to Alzheimer's disease biomarkers | #SNA #health

luiy's insight:

Due to the fact that the number of deaths due Alzheimer is increasing, the scientists have a strong interest in early stage diagnostic of this disease. Alzheimer's patients show different kind of brain alterations, such as morphological, biochemical, functional, etc. Currently, using magnetic resonance imaging techniques is possible to obtain a huge amount of biomarkers; being difficult to appraise which of them can explain more properly how the pathology evolves instead of the normal ageing. Machine Learning methods facilitate an efficient analysis of complex data and can be used to discover which biomarkers are more informative. Moreover, automatic models can learn from historical data to suggest the diagnostic of new patients. Social Network Analysis (SNA) views social relationships in terms of network theory consisting of nodes and connections. The resulting graph-based structures are often very complex; there can be many kinds of connections between the nodes. SNA has emerged as a key technique in modern sociology. It has also gained a significant following in medicine, anthropology, biology, information science, etc., and has become a popular topic of speculation and study. This paper presents a review of machine learning and SNA techniques and then, a new approach to analyze the magnetic resonance imaging biomarkers with these techniques, obtaining relevant relationships that can explain the different phenotypes in dementia, in particular, different stages of Alzheimer's disease.

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High-Resolution Maps of Science | #dataviz #scientometrics

High-Resolution Maps of Science | #dataviz #scientometrics | e-Xploration | Scoop.it

'Maps of science derived from citation data visualize the relationships among scholarly publications or disciplines. They are valuable instruments for exploring the structure and evolution of scholarly activity. Much like early world charts, these maps of science provide an overall visual perspective of science as well as a reference system that stimulates further exploration. However, these maps are also significantly biased due to the nature of the citation data from which they are derived: existing citation databases overrepresent the natural sciences; substantial delays typical of journal publication yield insights in science past, not present; and connections between scientific disciplines are tracked in a manner that ignores informal cross-fertilization..'


Via Nicholas Goubert, Lauren Moss, Rui Guimarães Lima
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Social Network Analysis of Science #Crowdfunding | #SNA

Social Network Analysis of Science #Crowdfunding | #SNA | e-Xploration | Scoop.it
CrowdFundingPlanning is a Complete Solution for a Fast and Successful CrowdFunding campaign empowering SMBs, Start-ups, Entrepreneurs, CF Expert Professionals and Investors Community.

Power of the Crowd, Expert and Cloud Sourcing.
luiy's insight:

Readers will remember when I announced Ethan Perlstein‘s plan to crowdfund his scientific research. Well, since then, Ethan has been combining two of my interests: alternative ways of funding science and network science. In his attempt to achieve his goal of raising $25,000, Ethan has been attempting to understand what conditions and connections yield the most money. And network analysis is one component of this.


Some of his analyses have looked at the statistical properties of the donations so far, confirming that donations do not come in at a constant rate (there is often a burst in the beginning and end, with some stagnation in the middle). In addition, Ethan recently emailed me an analysis based on his Facebook friends, and who donated and who did not:

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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 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, 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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#RINGS : A Technique for Visualizing Large Hierarchies | #SNA #dataviz

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Barnes, un #anthropologue à la « découverte » des réseaux sociaux | #DH #sna

Barnes, un #anthropologue à la « découverte » des réseaux sociaux | #DH #sna | e-Xploration | Scoop.it
luiy's insight:

Si l’on ne prête attention qu’à leur vogue récente, les « réseaux sociaux » auraient été inventés il y a une dizaine d’années en Californie par les fondateurs de Friendster, MySpace, LinkedIn, et bien sûr Facebook et Twitter. Mais si on se fie au contraire à la perspective théorique et méthodologique dessinée par la plupart des ouvrages d’introduction à l’analyse des réseaux sociaux(Wasserman et Faust, 1994 ; Lazega, 1995; Degenne et Forsé, 2004_ENREF_11 ; ENREF_9 Mercklé, 2011 ; Scott, 2012), leur existence serait en réalité aussi ancienne que l’humanité elle-même : à partir du moment où il y a des interactions entre individus et entre entités sociales, il y a des réseaux sociaux. Les approches historiographiques (Lemercier, 2005) font l’hypothèse qu’il y en avait dans la France du XIXe siècle (Gribaudi et Blum, 1990), dans l’Italie du XVe siècle (Padgett et Ansell, 1993), voire dans la Rome antique (Alexander et Danowski, 1990) ou le Néolithique méditerranéen (Brysbaert, 2011). Il serait plus exact de dire en réalité que ces approches ont fait, depuis une vingtaine d’années, l’hypothèse qu’il était pertinent de penser et de représenter sous la forme de « réseaux sociaux » des structures de relations sociales aussi anciennes que celles de la Renaissance ou du Néolithique.....

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Social Media #Analysis Reveals The Complexities Of Syrian #Conflict | #SNA

Social Media #Analysis Reveals The Complexities Of Syrian #Conflict | #SNA | e-Xploration | Scoop.it
Computer scientists have used the pattern of social media communication in Syria to reveal the structure of opposing forces in the civil war.
luiy's insight:

These guys studied over 600 Twitter and YouTube accounts that post or link to content related to the Syrian conflict. Since many of these accounts point to each other or similar content, they form communities amongst themselves. So O’Callaghan and co used a standard community detection algorithm to tease apart how the accounts were aligned.

 

The results reveal 16 separate communities which together form four clearly aligned groups. The first are Jihadist, made up of three communities and including accounts associated with Al-Qa’ida.

 

The second are Kurdish, consisting of a community of political parties and another of youth organisations.

 

The third is Pro-Assad and consists of essentially one community of supporters of the current Syrian regime.

 

The final group is made up of ten communities who are characterised as secular or moderate opposition. This includes accounts that support the Free Syrian Army and the Syrian National Coalition.

 

O’Callaghan and co go on to analyse a representative community from each group. For example, one community supporting the Free Syrian Army consists of 105 social media accounts including one with 73,000 followers that supplies photographs of unidentified bodies so that people can help identify them.

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Open source diagramming framework for Java | Datagr4m | #SNA #clustering

Open source diagramming framework for Java | Datagr4m | #SNA #clustering | e-Xploration | Scoop.it
luiy's insight:

Assigning layouts to structural data patterns generates diagrams closed to the domain model conventions.

2 ways for analysing data topologies:

 

1- top-down analysis: compute largest super-structures first, and refine content of each structure by computing internal sub-structures. 

 

2- bottom-up analysis: compute smallest sub-structures first, and then generate super-structures based on sub-structures patterns until no more super-structure is generated.

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Interpreting the networks of online conversation | #SNA #CollectiveIntelligence

Online conversations (and even offline ones) are, deep down, networks. But how to visualize network data so that they make sense to non-network scientists? Pres
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Marco Valli's curator insight, February 3, 1:02 PM

As the title suggests...interesting approach! The analysis is clear...I just wonder "how can this be of any help"?

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#BigData Investment Map 2014 | #dataviz #SNA via @furukama

#BigData Investment Map 2014 | #dataviz #SNA via @furukama | e-Xploration | Scoop.it

by BENEDIKT KOEHLER on 1. FEBRUAR 2014

luiy's insight:

Here’s an updated version of our Big Data Investment Map. I’ve collected information about ca. 50 of the most important Big Data startups via the Crunchbase API. The funding rounds were used to create a weighted directed network with investments being the edges between the nodes (investors and/or startups). If there were multiple companies or persons participating in a funding round, I split the sum between all investors.

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How Information Flows During #Emergencies | #SNA #patterns

How Information Flows During #Emergencies |  #SNA #patterns | e-Xploration | Scoop.it
Mining the mobile phone data from 10 million people over 4 years reveals the subtle changes that occur in the flow of information when disaster strikes, say network scientists.
luiy's insight:

In particular, they studied the communications behaviour or two groups of people. The first consists of people close enough to the emergency event to be directly influenced by it. The second are the group of people called by the first group, presumably made up largely of close friends and relatives.

 

Since the question Liang and co want to examine is how the communication behaviour of both groups change during the emergency, they also study how people behave in ordinary circumstances, such as during a concert.

 

When an emergency occurs, there is an immediate spike in activity from the first group as they call or text their friends and relatives about the situation. At the same time, the activity of the second group also spikes.

 

--------------

 

Liang and co conclude that the need for correspondence with eyewitnesses is more critical than the dissemination of situational awareness during emergencies.” In other words, the desire to want to find out more trumps the need to pass on what they already know. At least in emergency situations.

 

Ref:  arxiv.org/abs/1401.1274: Quantifying Information Flow During Emergencies

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Using #NodeXL to decipher #bigdata | #SNA

Using #NodeXL to decipher #bigdata | #SNA | e-Xploration | Scoop.it
Quirks Marketing Research Review magazine, January 2014. Featuring articles on online research - among others.
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