Social Network Analysis #sna
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Social Network Analysis #sna
Social Network Analysis
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The Real Difference Between Google And Apple

The Real Difference Between Google And Apple | Social Network Analysis #sna | Scoop.it
Google and Apple are both hyper-successful companies, but chart their patents, and they have completely different innovation signatures.
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Gephi 3D Network Analysis - Tweets Mentioning Donald Trump  

I'm experimenting with some video for my presentation at the 2016 American Political Science Convention over the weekend of September 1st through 4t
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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Social Network Analysis #sna | Scoop.it

Via luiy
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luiy's curator insight, March 27, 2014 1:44 PM

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

António F Fonseca's curator insight, April 2, 2014 6:01 AM

Another paper about popularity prediction.

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Does the internet promote fairness of income distribution? (w/ Video) | #SNA #patterns

Does the internet promote fairness of income distribution? (w/ Video) | #SNA #patterns | Social Network Analysis #sna | Scoop.it
(Phys.org) —The question of how an economic system should be structured in order to best promote fairness and equality is one of the most debated subjects of all time. By approaching the complexities of this question from the field of network science, researchers from MIT and other institutions have ...

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luiy's curator insight, February 10, 2014 1:52 PM

In their study, the researchers constructed a model in which individuals can earn income in two ways: by producing content or by distributing the content produced by others. A system in which more income is earned by production than by distribution is labeled as meritocratic, while one in which more income is earned by distribution is called topocratic. Importantly, the income earned by distribution depends not on what an individual produces but rather on an individual's position in the network.

 

Using this simple model, the researchers showed that the connectivity of the network determines whether the income is earned in a meritocratic or topocratic manner: densely connected networks are more meritocratic, while sparsely connected networks are more topocratic.

 

The difference makes sense, since individuals in densely connected networks can sell what they produce directly to others, and therefore do not need to share much of their proceedings with middlemen. On the other hand, in sparsely connected networks, individuals do not have direct connections with buyers and must rely on middlemen to help them connect.


Read more at: http://phys.org/news/2014-01-internet-fairness-income-video.html#jCp

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Mapping Medieval Networks. Relational Perspectives on the Late Medieval Balkans (1200-1600 AD)

Mapping Medieval Networks. Relational Perspectives on the Late Medieval Balkans (1200-1600 AD) | Social Network Analysis #sna | Scoop.it

""We intend to highlight the relational character of various phenomena of pre-modern societies which can be best modelled as complex networks for the purpose of analysis on the basis of case-studies from the late medieval Balkans"

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How to: network animation with R and the iGraph package & Meaning in data viz

How to: network animation with R and the iGraph package & Meaning in data viz | Social Network Analysis #sna | Scoop.it

This article lists the steps I take to create a network animation in R, provides some example source code that you can copy and modify for your own work, and starts a discussion about programming and visualization as an interpretive approach in research.

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“Drug Deal” Network Analysis with Gephi (Tutorial)

“Drug Deal” Network Analysis with Gephi (Tutorial) | Social Network Analysis #sna | Scoop.it

"If you had a big dataset that you prepped into a trimmed nearest neighbors graph, keep in mind that visualizing it in Gephi is just for fun. It’s not necessary for actual insight regardless of what the scads of presentations of tweets-spreading-as-visualized-in-Gephi might tell you (gag me). You just need to do the community detection piece. You can use Gephi for that or the libraries it uses. R and python both have a package called igraph that does this stuff too. Whatever you use, you just need to get community assignments out of your large dataset so that you can run things like the aggregate analysis over them to bubble up intelligence about each group."

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My Network Revealed - Now what can you learn about yours?

My Network Revealed - Now what can you learn about yours? | Social Network Analysis #sna | Scoop.it

So when you map your world, check these issues:

 

Are you happy with how diverse it is? If it is not diverse you will find it hard to use crowd sourcing etc and you will be too deep in your own echo chamber

 

Do you know who your key connectors are and do you care enough about them? For they give you the best access to the sub networks?

 

Do you know who counts the most in each network? Again this is all about leverage.

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Professor Wayne Baker: NETWORKS, ORGANIZATIONS AND ECONOMIC CRIME

Role as Resource in the Hollywood Film Industry (American Journal of Sociology)

 

"Racial Segregation and the Digital Divide in the Detroit Metropolitan Region,"

 

Social Networks and Loss of Capital (Social Networks)

 

Crime by Committee: Conspirators and Company Men in the Illegal Electrical Industry Cartel, 1954-1959 (Criminology)

 

Diffusion of Fraud: Intermediate Economic Crime and Investor Dynamics (Criminology)

 

The Social Organization of Conspiracy: Illegal Networks in the Heavy Electrical Equipment Industry (American Sociological Review)

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Social network analysis of Twitter hashtag usage during protests in Russia

Alexander Semenov's slides from ASNA2012 with some findings from dataset I've gathered from Twitter during protest meetings in Moscow on 24th of December 2011

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Network20Q - YouTube Channel for the Coursera course on networks analysis

Network20Q - YouTube Channel for the Coursera course on networks analysis | Social Network Analysis #sna | Scoop.it

Twitter page for the course is http://twitter.com/network20q

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Computational social science: Making the links

Computational social science: Making the links | Social Network Analysis #sna | Scoop.it
From e-mails to social networks, the digital traces left by life in the modern world are transforming social science.
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Introduction to Social Network Methods:  Chapter 7:  Basic Properties of Networks and Actors

Introduction to Social Network Methods:  Chapter 7:  Basic Properties of Networks and Actors | Social Network Analysis #sna | Scoop.it

Via Katy Jordan
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Connected World: Untangling the Air Traffic Network

Connected World: Untangling the Air Traffic Network | Social Network Analysis #sna | Scoop.it
THE NETWORK




CC-BY-SA Freely reusable with a link to this post

 This
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PLOS ONE #Complex systems articles | #ABM #netwoks #research

PLOS ONE #Complex systems articles | #ABM #netwoks #research | Social Network Analysis #sna | 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 Bryan A. Knowles, Bernard Ryefield, Luciana Viter, Roger D. Jones, PhD, luiy
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graph-tool: Efficent network analysis with python I #SNA #python

graph-tool: Efficent network analysis with python I #SNA #python | Social Network Analysis #sna | Scoop.it
graph-tool: Efficent network analysis with python

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luiy's curator insight, November 19, 2013 9:04 AM

Graph-tool is an efficient Python module for manipulation and statistical analysis ofgraphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance which is comparable (both in memory usage and computation time) to that of a pure C/C++ library.

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Company Restructuring Visualized

“ Unless you’re self-employed, we’re all cogs in a larger machine. The problem can be, how do you track that machine’s anatomy? How do you know what a “restructuring” really looks like, beyond that your middle manager has a new face?"

 

ukituki's insight:

 

Justin Matejka wondered the same thing after his relatively small employer Alias was acquired by Autodesk, so he created this remarkable animated visualization called the OrgOrgChart (Organic Organization Chart), which tracks every employee, manager, and departmental shift in the company from May 2007 to April 2011. Each second represents a week of activity for a growing staff, which expands from 6,500 to 7,500 people over this time.

 
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Andries Du Plessis's comment, February 27, 2013 10:59 AM
In the light of many layoffs, also in South Africa, the effects are ill-understood by managers and the rest of the staff
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Three Classical Social Network Datasets Now Available: Zachary Karate Club, Southern Women and Tribes of New Guinea – WeST Blog (University of Koblenz-Landau) | WeST Blog

Before there was the World Wide Web, social network analysis needed to be done using datasets collected by hand.
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Summary of community detection algorithms

In this post, we’ll cover the community detection algorithms (~i.e., clustering, partitioning, segmenting) available in igraph 0.6 and their characteristics, such as their worst-case runtime performance and whether they support directed or weighted edges.

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ABC: Always Be Closing - the network edition

ABC: Always Be Closing - the network edition | Social Network Analysis #sna | Scoop.it

What is a high quality network that learns and reacts well?

 

Too many density is not good. But too little density is not a good thing either.

 

The trick is to close a lot in the centre - the centre should be densely connected - so we don’t have to go through only you and so avoid being a bottleneck - closed triangles = trust and speed


But you don’t want to close ALL your triangles - risk then you end up with an echo chamber

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Social Networks, Individuals and Small Worlds

Guest lecture at Free University in Knowledge Management course by Maura Soekijad and Roos Erkelens

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The Social Networks of Myths

The Social Networks of Myths | Social Network Analysis #sna | Scoop.it

Social-network analysis of the characters in mythological texts reveal how realistic the myths are, by looking at how closely they ressemble the patterns of real social-networks. Realistic myths can reveal clues about their respective ancient civilization.

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Wirearchy - The Network Thinkers

Wirearchy - The Network Thinkers | Social Network Analysis #sna | Scoop.it

"Wirearchy is a dynamic two-way flow of power and authority based on:


knowledge,
trust,
credibility,
a focus on results

 

enabled by interconnected people and technology"
-Jon Husband, 1999

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Knowledge about the Networks Analysis goes mainstream

Knowledge about the Networks Analysis goes mainstream | Social Network Analysis #sna | Scoop.it
This is the proof that knowledge about the networks analysis is going mainstream. Just on Coursera alone there are 5 courses starting in September’12 that will cover different aspects of networks. And...
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