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Identifying Communities in Traffic Flow

Identifying Communities in Traffic Flow | Social Network Analysis #sna | Scoop.it

"Community detection algorithms were developed to identify clusters within a network dataset. These methods are most often applied to examples within the social network sphere, in the identification of cliques, where a cluster demonstrates high inter-connectivity, with lower connectivity with the rest of the network. My thinking behind this bit of work was that we might be able to identify similar characteristics in traffic flow, where we can observed high coupling between clusters of nodes.

 

The map below visualises the modules (distinguished by colour) identified through the application of community detection methods to a topological representation of the road network. Node connectivity is established using a dataset of 1.5 million private hire cab routes through London."

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Social Network Analysis #sna
Social Network Analysis
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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...
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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
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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

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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.
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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.
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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. 

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Thanksgiving ingredient network leftovers

Thanksgiving ingredient network leftovers | Social Network Analysis #sna | Scoop.it
Michaeleen Doucleff just wrote a very fun article on our recipe network paper for NPR’s the Salt. It made me realize that Edwin Teng, Yuru Lin and I have some leftover plots that may be Thanksgiving appropriate. If you don’t have quite the right ingredients handy while cooking Thanksgiving dinner, here is a network of [...]
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Alexander Semenov's curator insight, November 29, 8:15 PM

Our International laboratory of applied social network analysis has similar project

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See how red tweeters and blue tweeters ignore each other on Ferguson

See how red tweeters and blue tweeters ignore each other on Ferguson | Social Network Analysis #sna | Scoop.it
There are red tweets and blue tweets…
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Alexander Semenov's curator insight, December 17, 2:51 AM

I haven't seen a meaningful hairball in a while. Here is one of them: echo-chamber effect in tweets about Ferguson.

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Data science with F#: Social network analysis - Twitter case study by @evelgab

ukituki's insight:

In this session we will work through the whole process of social network analysis: from downloading connections using Twitter REST-based API, to implementing our own PageRank algorithm which finds the most central Twitter accounts. In the process you’ll see how we can use F# type providers to access data and harness the power of the statistical language R to run some machine learning algorithms.

At the end, you’ll know how to run your own analysis on data from Twitter and how to use data science tools to gain insights from social networks.

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The Art of Financial Network Science

The Art of Financial Network Science | Social Network Analysis #sna | Scoop.it
ukituki's insight:

There was an amazing array of images of financial networks at the seminar on Financial Risk and Network Theory to launch the new journal on Network Theory in Finance. Have a look at these visualizations from some of the 23 presentations.

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The mathematician who proved why hipsters all look alike

The mathematician who proved why hipsters all look alike | Social Network Analysis #sna | Scoop.it
What brain neurons tell us about the 'hipster effect' when all contrarians end up looking the same.
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R Graph Catalog

R Graph Catalog | Social Network Analysis #sna | Scoop.it
ukituki's insight:

This catalog is a complement to “Creating More Effective Graphs” by Naomi Robbins. All graphs were produced using the Rlanguage and the add-on packageggplot2, written by Hadley Wickham. The gallery is maintained by Joanna Zhao andJennifer Bryan.

 
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Engineering Serendipity: The Story of Web Summit's Growth

Engineering Serendipity: The Story of Web Summit's Growth | Social Network Analysis #sna | Scoop.it
It’s taken us 4 years to scale Web Summit from 400 attendees to 20,000 and a bunch of physicists have played a big part. Back in 2010, 3 international journalists showed up, this year it will exceed 1,200. Investors...
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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”
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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

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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.
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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
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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.
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72 Hours of #Gamergate — The Message — Medium

72 Hours of #Gamergate — The Message — Medium | Social Network Analysis #sna | Scoop.it
Digging through 316,669 tweets from three days of Twitter’s two-month-old trainwreck
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Existence of outsiders as a characteristic of online communication networks

ukituki's insight:
Online social networking services (SNSs) involve communication activities between large number of individuals over the public Internet and their crawled records are often regarded as proxies of real (i.e., offline) interaction structure. However, structure observed in these records might differ from real counterparts because individuals may behave differently online and non-human accounts may even participate. To understand the difference between online and real social networks, we investigate an empirical communication network between users on Twitter, which is perhaps one of the largest SNSs.
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Social and Economic Networks (book).

Social and Economic Networks (book). | Social Network Analysis #sna | Scoop.it
Description of the book Social and Economic Networks by Jackson, M.O., published by Princeton University Press
ukituki's insight:


Networks of relationships help determine the careers that people choose, the jobs they obtain, the products they buy, and how they vote. The many aspects of our lives that are governed by social networks make it critical to understand how they impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. In Social and Economic Networks, Matthew Jackson offers a comprehensive introduction to social and economic networks, drawing on the latest findings in economics, sociology, computer science, physics, and mathematics. He provides empirical background on networks and the regularities that they exhibit, and discusses random graph-based models and strategic models of network formation. He helps readers to understand behavior in networked societies, with a detailed analysis of learning and diffusion in networks, decision making by individuals who are influenced by their social neighbors, game theory and markets on networks, and a host of related subjects. Jackson also describes the varied statistical and modeling techniques used to analyze social networks. Each chapter includes exercises to aid students in their analysis of how networks function.

 

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Social Ties in the Digital Age by @ognyanova

Core discussion ties in America - recent changes and the role of technology.
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Cross social network analysis for building quality data sets | Log for incrementing randomities

Cross social network analysis for building quality data sets | Log for incrementing randomities | Social Network Analysis #sna | Scoop.it
ukituki's insight:

When it comes to machine learning, building quality training data can be the toughest part of your work. Our goal was to train a SVM classifier that would guess the demographics of a given twitter user based on the content of their tweets, namely their age, gender, education(low/mid/high) and location (city/county). For that we needed a data set containing profiles and their demographics. To simplify the problem, we decided to limit ourselves to users located in the UK, and to tweets written in English.

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