Social Network An...
Follow
9.4K views | +0 today
 
Rescooped by ukituki from Network Science
onto Social Network Analysis #sna
Scoop.it!

Consensus clustering in complex networks

Consensus clustering in complex networks | Social Network Analysis #sna | Scoop.it

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods.


Via Complexity Digest, Spaceweaver, David Rodrigues
more...
No comment yet.

From around the web

Social Network Analysis #sna
Social Network Analysis
Curated by ukituki
Your new post is loading...
Your new post is loading...
Rescooped by ukituki from Augmented Collective Intelligence
Scoop.it!

The Collective Intelligence Handbook (MIT CCI)

The Collective Intelligence Handbook (MIT CCI) | Social Network Analysis #sna | Scoop.it

"

The Collective Intelligence Handbook [tentative title]Thomas W. Malone and Michael S. Bernstein (Editors)

Collective intelligence has existed at least as long as humans have, because families, armies, countries, and companies have all--at least sometimes--acted collectively in ways that seem intelligent. But in the last decade or so a new kind of collective intelligence has emerged: groups of people and computers, connected by the Internet, collectively doing intelligent things. In order to understand the possibilities and constraints of these new kinds of intelligence, a new interdisciplinary field is emerging.

This book will introduce readers to many disciplinary perspectives on behavior that is bothcollective and intelligent. By collective, we mean groups of individual actors, including, for example, people, computational agents, and organizations. By intelligent, we mean that the collective behavior of the group exhibits characteristics such as, for example, perception, learning, judgment, or problem solving."


Via Claude Emond, Howard Rheingold
more...
Carine Garcia's curator insight, June 16, 2014 4:44 AM

This book will introduce readers to many disciplinary perspectives on behavior that is bothcollective and intelligent. The goal of this edited volume is to help catalyze research in the field of collective intelligence by laying out a shared set of research challenges and methodological perspectives.

Geemik's curator insight, June 17, 2014 5:29 AM

"This book will introduce readers to many disciplinary perspectives on behavior that is both collective and intelligent.  By collective, we mean groups of individual actors, including, for example, people, computational agents, and organizations.  By intelligent, we mean that the collective behavior of the group exhibits characteristics such as, for example, perception, learning, judgment, or problem solving. "

Tannah Gravelis's curator insight, August 22, 2014 4:20 AM

This article is an extremely good look into what the modern day era has created in terms of collective intelligence. The article comes from an extremely credible source, and is an extensive and comprehensive study into the topic. I\Of all the articles i have found, this is the one I would recommend to someone who wanted to gain a better understanding on the topic.

 

Rank = 1

Rescooped by ukituki from The science toolbox
Scoop.it!

College Faculties Have a Serious Diversity Problem | WIRED

College Faculties Have a Serious Diversity Problem | WIRED | Social Network Analysis #sna | Scoop.it
“ To be a professor is to belong to a select few—an insider’s club of vanishing tenured faculty positions. It’s no secret that a fancy diploma can help grads vying for those coveted spots. But while working on his PhD and contemplating his career prospects, computer scientist Aaron Clauset wanted to know just how much weight a…”
Via Niklaus Grunwald
more...
No comment yet.
Scooped by ukituki
Scoop.it!

Understanding users through Twitter data and machine learning | Twitter Blogs

Understanding users through Twitter data and machine learning | Twitter Blogs | Social Network Analysis #sna | Scoop.it
MonkeyLearn is a text mining platform that uses machine learning to help developers easily extract and classify information from text. Here’s how to use the MonkeyLearn API to analyze Twitter data to understand user interests.
more...
No comment yet.
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...
No comment yet.
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, 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.
Rescooped by ukituki from Social Simulation
Scoop.it!

Science vs Conspiracy: Collective Narratives in the Age of Misinformation

Science vs Conspiracy: Collective Narratives in the Age of Misinformation | Social Network Analysis #sna | Scoop.it
“ The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories—e.g., chemtrails, reptilians or the Illuminati—are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives—i.e. main stream scientific and conspiracy news—are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users’ content selection, we conclude our analysis measuring how users respond to 4,709 troll information—i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.”
Via Ashish Umre, Frédéric Amblard
ukituki's insight:
We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.
more...
No comment yet.
Scooped by ukituki
Scoop.it!

What Connects Dutch Corporates? Linked Innovation in the Netherlands « Kenedict Innovation Analytics

What Connects Dutch Corporates? Linked Innovation in the Netherlands « Kenedict Innovation Analytics | Social Network Analysis #sna | Scoop.it
more...
No comment yet.
Scooped by ukituki
Scoop.it!

Serious network analysis using Hadoop and Neo4j - Neo4j Graph Database

Serious network analysis using Hadoop and Neo4j - Neo4j Graph Database | Social Network Analysis #sna | Scoop.it

Friso van Vollenhoven of Xebia uses a combination of Hadoop, Neo4j and browser based visualization and interactive tools to look at graphs, search for known interesting patterns in big graphs and do ad hoc querying against graphs.

ukituki's insight:

In this video, he demonstrates the workflow of using Hadoop to create a graph out of data and bulk load the result into Neo4j for efficient ad hoc querying and visualization, potentially partitioning the graph in Hadoop, to create partitions of manageable volume for the database

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.
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.