Social Network Analysis #sna
12.5K views | +0 today
Social Network Analysis #sna
Social Network Analysis
Curated by ukituki
Your new post is loading...
Your new post is loading...
Scooped by ukituki
Scoop.it!

Discovering the relationship of the G20 members using Data Mining

Discovering the relationship of the G20 members using Data Mining | Social Network Analysis #sna | Scoop.it
It takes just a little talk with me to know that I'm a fan of the financial market and many subjects related to economics.  
ukituki's insight:

Given all these data, we conclude that the present relations in economic news actually reflect the data from our commercial relations. Maybe it was not different, but it is a way to show how everything is connected and in fact, given that markets are efficient (there is much discussion here and I tend to disagree with the theory), we have that trade relations will be reflected in some way in the behavior of market players and consequently,will be reflected upon pricing of financial assets.

more...
No comment yet.
Scooped by ukituki
Scoop.it!

Data Mining Reveals a Global Link Between Corruption and Wealth

Data Mining Reveals a Global Link Between Corruption and Wealth | Social Network Analysis #sna | Scoop.it
Social scientists have never understood why some countries are more corrupt than others. But the first study that links corruption with wealth could help change that.
ukituki's insight:

Paulus and Kristoufek use this data to search for find clusters of countries that share similar properties using a new generation of cluster-searching algorithms. And they say that the 134 countries they study fall neatly into four groups which are clearly correlated with the wealth of the nations within them.

The method that makes this possible is known as the average linkage clustering approach. It begins by assuming that each country represents a cluster in itself and then looking for the nearest neighbour in the ranking. This pair then become a new cluster and this cluster placed back into the list as a single entity. The process is then repeated until it produces a single cluster of all the countries.

more...
A. J. Alvarez-Socorro's curator insight, May 14, 2015 1:01 AM

Interesting :D

Eli Levine's curator insight, May 14, 2015 2:12 PM

The question then becomes whether wealth, or the lack thereof, is a cause of corruption or wealth is caused by a lack of corruption.  It makes sense in my mind that a lack of corruption leads to increased potential to make wealth because the appropriate use of resources (ie, using resources for their stated purposes) and the honesty that underlies that point could, in my view, make for more wealth than the dishonest methods and inappropriate usage of public funds would.  We could hypothetically run simulations to test which one is the case, similar to the Sugarscape experiments.  On the other hand, a lack of resources may incentivize those who don't have much to take more from the public resources and use them for private uses (ie, what I call the Jean Valjean effect).  More experimentation should be done to tease this out.  Perhaps by knowing what they're giving up by having corruption, the officials will be more circumspect about how they live off and take from the public till.

Scooped by ukituki
Scoop.it!

Meetup Analytics with R and Neo4j

The majority of NoSQL meetups in London are hosted on meetup.com and luckily for us meetup.com has an API that allows us to extract all the corresponding data

more...
No comment yet.
Scooped by ukituki
Scoop.it!

G20 Twitter Communities

G20 Twitter Communities | Social Network Analysis #sna | Scoop.it

Introduction Last weekend the G20 Conference was hosted in Brisbane Australia. According to the G20 official website the objectives of G20 are: The Group of Twenty (G20) is the premier forum for its members’ international economic cooperation and decision-making

more...
No comment yet.
Scooped by ukituki
Scoop.it!

Davos on Twitter: who do the attendees follow?

Davos on Twitter: who do the attendees follow? | Social Network Analysis #sna | Scoop.it
ukituki's insight:

Network Visualization by Finanacial Times

more...
luiy's curator insight, January 29, 2015 5:48 AM

Every year, the World Economic Forum brings together the most recognisable figures of business and politics. With all eyes on Davos, we decided to turn the optics upside down and see who the twitterati gathered in Switzerland follow on social media.


The inner ring of circles represent the 20 most-followed accounts by Davos attendees, while the outer circles are individual attendees.

Scooped by ukituki
Scoop.it!

Restaurant Business: Everyone Knows Everyone.

Restaurant Business: Everyone Knows Everyone. | Social Network Analysis #sna | Scoop.it
It’s uncanny how many New York chefs and restaurateurs have worked with the same handful of people.
more...
No comment yet.
Scooped by ukituki
Scoop.it!

Wikipedia Mining Algorithm Reveals The Most Influential People In 35 Centuries Of Human History

Wikipedia Mining Algorithm Reveals The Most Influential People In 35 Centuries Of Human History - The Physics arXiv Blog - Medium
The top ranked men and women will surprise you
more...
No comment yet.
Scooped by ukituki
Scoop.it!

What interests reddit?

What interests reddit? | Social Network Analysis #sna | Scoop.it
“Mark Allen Thornton, Psychology Ph.D. Candidate in the Social Cognitive and Affective Neuroscience Lab at Harvard University”
more...
No comment yet.
Scooped by ukituki
Scoop.it!

Wang: The Role of the Director Social Networks in Spreading Misconduct

Wang: The Role of the Director Social Networks in Spreading Misconduct | Social Network Analysis #sna | Scoop.it

ABSTRACT: After 2000, a growing number of foreign firms list in the United States through reverse merger, a non-IPO listing technique that requires less information disclosure. Are the US regulations rigorous enough to deter the listing attempts of weak foreign firms? 

ukituki's insight:

Using a social network analysis, I find that the firms are assisted by Western professionals to help them circumvent the US regulations, and they commit fraud and benefit from fast stock sales after listing. Further, I find that the social network of the linked directors facilitates the spread of their misconduct. During the wrongdoers’ listings, the investors in these firms lost at least $811 million. However, the penalties charged to the wrongdoers only accounted for 4.19% of this loss. I also find that the US-listed Chinese firms have a lower average Tobin’s q compared to the China-listed firms, in contrast to the prior research’s findings. These findings contradict the concurrent research that uses the reverse mergers’ financial data, which proves to be unreliable

more...
No comment yet.
Scooped by ukituki
Scoop.it!

Social network analysis: Centrality measures

Centrality measures: What they are, what they tell us and when to use them. Degree centrality, betweenness centrality and closeness centrality summarized.
more...
No comment yet.
Scooped by ukituki
Scoop.it!

Intellectual Cooperation: multi-level network analysis of an international organization

Intellectual Cooperation: multi-level network analysis of an international organization | Social Network Analysis #sna | Scoop.it
Digital humanities, Data visualization, Network analysis
more...
No comment yet.
Scooped by ukituki
Scoop.it!

Fascinating maps of what people tweet about in Istanbul, Baltimore, Barcelona and more

Fascinating maps of what people tweet about in Istanbul, Baltimore, Barcelona and more | Social Network Analysis #sna | Scoop.it
Dave Troy crunches data to see places not as neighborhoods but as relationships between people.
more...
No comment yet.
Scooped by ukituki
Scoop.it!

Opinion formation driven by PageRank node influence on directed networks

We study a two states opinion formation model driven by PageRank node influence and report an extensive numerical study on how PageRank affects collective opinion formations in large-scale empirical directed networks. In our model the opinion of a node can be updated by the sum of its neighbor nodes' opinions weighted by the node influence of the neighbor nodes at each step. 

ukituki's insight:

First, we observe that all networks reach steady state opinion after a certain relaxation time. This time scale is decreasing with the heterogeneity of node influence in the networks. Second, we find that our model shows consensus and non-consensus behavior in steady state depending on types of networks: Web graph, citation network of physics articles, and LiveJournal social network show non-consensus behavior while Wikipedia article network shows consensus behavior. Third, we find that a more heterogeneous influence distribution leads to a more uniform opinion state in the cases of Web graph, Wikipedia, and Livejournal. However, the opposite behavior is observed in the citation network. Finally we identify that a small number of influential nodes can impose their own opinion on significant fraction of other nodes in all considered networks.

more...
No comment yet.
Rescooped by ukituki from Data science
Scoop.it!

Maps of Citations Uncover New Fields of Scholarship - Research - The Chronicle of Higher Education

Maps of Citations Uncover New Fields of Scholarship - Research - The Chronicle of Higher Education | Social Network Analysis #sna | Scoop.it
“ Lovely visualization of interdisciplinary citations (really, directed weighted graph) and changes in network clustering http://t.co/o2vZyVQJ...”;
Via Davide
more...
No comment yet.
Scooped by ukituki
Scoop.it!

The tribes of Davos

The tribes of Davos | Social Network Analysis #sna | Scoop.it
By David Blood and Aleksandra Wisniewska
More than 2,500 people are attending the World Economic Forum in Davos this week, but how are they connected outside of the picturesque alpine town?
 Read more
more...
No comment yet.
Rescooped by ukituki from Connectivism
Scoop.it!

Connectivism Chart

Connectivism Chart | Social Network Analysis #sna | Scoop.it

Via Dr. Susan Bainbridge
more...
Silvan Pan Morel's curator insight, January 17, 2015 10:51 PM

añada su visión ...

Scooped by ukituki
Scoop.it!

10 Types of Odd Friendships You're Probably Part Of | Wait But Why

10 Types of Odd Friendships You're Probably Part Of | Wait But Why | Social Network Analysis #sna | Scoop.it
When you're young, you make friends kind of by accident. Then they stick.
more...
No comment yet.
Scooped by ukituki
Scoop.it!

Pseudo Feature Extraction in Social Network Analysis and Text Mining

Pseudo Feature Extraction in Social Network Analysis and Text Mining | Social Network Analysis #sna | Scoop.it
This is a webinar I delivered as a part of a webinar series entitled "We are all Social Things" organized by the IS department at King Saud University - Fema...
more...
No comment yet.
Rescooped by ukituki from Bounded Rationality and Beyond
Scoop.it!

Applause is Contagious Like a Disease - D-brief

Applause is Contagious Like a Disease - D-brief | Social Network Analysis #sna | Scoop.it
“ Applause spreads linearly, like a disease. The amount of time an individual feels like clapping is a factor, but not nearly as much as peer pressure.”
Via Sakis Koukouvis, Complexity Institute, Alessandro Cerboni
more...
Cat Perrin's curator insight, July 12, 2013 6:11 AM

La foule.. et son effet de masse...

robyns tut's curator insight, October 14, 2013 1:04 PM

This is interesting, how peer pressure can factor into little things. Would be good to see what makes the brain do these things and what chemical reactions occure.

-Tanah

Scooped by ukituki
Scoop.it!

Social networks in primates: smart and tolerant species have more efficient networks

Social networks in primates: smart and tolerant species have more efficient networks | Social Network Analysis #sna | Scoop.it
Network optimality has been described in genes, proteins and human communicative networks. In the latter, optimality leads to the efficient transmission of information with a minimum number of connections. Whilst studies show that differences in centrality exist in animal networks with central individuals having higher fitness, network efficiency has never been studied in animal groups. Here we studied 78 groups of primates (24 species). We found that group size and neocortex ratio were correlated with network efficiency. Centralisation (whether several individuals are central in the group) and modularity (how a group is clustered) had opposing effects on network efficiency, showing that tolerant species have more efficient networks. Such network properties affecting individual fitness could be shaped by natural selection. Our results are in accordance with the social brain and cultural intelligence hypotheses, which suggest that the importance of network efficiency and information flow through social learning relates to cognitive abilities.
more...
No comment yet.
Scooped by ukituki
Scoop.it!

What can we learn from the history of social network analysis? | Knight Lab | Northwestern University

What can we learn from the history of social network analysis? | Knight Lab | Northwestern University | Social Network Analysis #sna | Scoop.it
Northwestern University Knight Lab is a team of technologists and journalists working at advancing news media innovation through exploration and experimentation.
more...
No comment yet.
Scooped by ukituki
Scoop.it!

Network visualization – Gephi fun with R

Network visualization – Gephi fun with R | Social Network Analysis #sna | Scoop.it
ukituki's insight:

In the second part of my “how to quickly visualize networks directly from R” series, I’ll discuss how to use R and the “rgexf” package to create network plots in Gephi. Gephi is a great network visualization tool that allows real-time network visualization and exploration, including network data spatializing, filtering, calculation of network properties, and clustering. 

more...
No comment yet.