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The rich club phenomenon in the classroom : Scientific Reports : Nature Publishing Group

The rich club phenomenon in the classroom : Scientific Reports : Nature Publishing Group | Social Network Analysis #sna | Scoop.it
We analyse the evolution of the online interactions held by college students and report on novel relationships between social structure and performance.
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Social Network Analysis #sna
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Efficiently inferring community structure in bipartite networks

Efficiently inferring community structure in bipartite networks | Social Network Analysis #sna | Scoop.it
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to $k$-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model's ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.
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Social Network of World Cup 2014 Players

Social Network of World Cup 2014 Players | Social Network Analysis #sna | Scoop.it
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FIFA World Cup 2014, the biggest sporting event in four years (sorry Olympics) is starting today. The tournament holds 736 players from 32 countries. When the players are not playing for their national teams, they play in 301 different clubs. Players from different national teams meet in these clubs. For example, Manchester United has players from 9 different national teams. This means that players in the World Cup who play in Manchester United know players from at least eight different national teams. Why is this important? If two players belong to the same team (national or club), they have a social connection. Using 

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Mini Lecture: Social Network Analysis for Fraud Detection

Mini Lecture: Social Network Analysis for Fraud Detection | Social Network Analysis #sna | Scoop.it
In this mini lecture, Véronique Van Vlasselaer talks about how social networks can be leveraged to uncover fraud. Véronique is working in the DataMiningApps group led by Prof. dr. Bart Baesens at the KU Leuven (University of Leuven), Belgium.
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Learn and talk about Social network analysis, Community building, Cultural economics, Information economics, Methods in sociology

Learn and talk about Social network analysis, Community building, Cultural economics, Information economics, Methods in sociology | Social Network Analysis #sna | Scoop.it
Learn and talk about Social network analysis, and check out Social network analysis on Wikipedia, Youtube, Google News, Google Books, and Twitter on Digplanet. Digplanet gathers together information and people from all over the Internet, all focused on Social network analysis, and makes it easy to learn, explore, and join the Digparty and talk to real people who are also interested in Social network analysis.
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Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University - YouTube

Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University - YouTube | Social Network Analysis #sna | Scoop.it

This course of 25 lectures, filmed at Cornell University in Spring 2014, is intended for newcomers to nonlinear dynamics and chaos. It closely follows Prof. Strogatz's book, "Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering." The mathematical treatment is friendly and informal, but still careful. Analytical methods, concrete examples, and geometric intuition are stressed. The theory is developed systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations, chaos, iterated maps, period doubling, renormalization, fractals, and strange attractors. A unique feature of the course is its emphasis on applications. These include airplane wing vibrations, biological rhythms, insect outbreaks, chemical oscillators, chaotic waterwheels, and even a technique for using chaos to send secret messages. In each case, the scientific background is explained at an elementary level and closely integrated with the mathematical theory. The theoretical work is enlivened by frequent use of computer graphics, simulations, and videotaped demonstrations of nonlinear phenomena. The essential prerequisite is single-variable calculus, including curve sketching, Taylor series, and separable differential equations. In a few places, multivariable calculus (partial derivatives, Jacobian matrix, divergence theorem) and linear algebra (eigenvalues and eigenvectors) are used. Fourier analysis is not assumed, and is developed where needed. Introductory physics is used throughout. Other scientific prerequisites would depend on the applications considered, but in all cases, a first course should be adequate preparation

 

Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University

https://www.youtube.com/playlist?list=PLbN57C5Zdl6j_qJA-pARJnKsmROzPnO9V


Via Complexity Digest
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Jean-Michel Livowsky's curator insight, June 2, 3:22 AM

Nonlinear Dynamics and Chaos...

Jean-Michel Livowsky's curator insight, June 2, 3:23 AM

Nonlinear Dynamics and Chaos

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Bitcoin Transaction Network Dataset

Bitcoin Transaction Network Dataset | Social Network Analysis #sna | Scoop.it

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luiy's curator insight, May 27, 11:09 AM

1.  Overview:

 

Bitcoin (bitcoin.org) is a digital, cryptographically secure currency. Transactions between public-key "addresses" maintained in a distributed, verified public ledger form a transaction network that can be studied by network scientists. This code processes binary-format Bitcoin .dat files generated by the Bitcoin client (bitcoin.org, tested on v0.5.3.1 or lower) into human-readable flat-file formats, retaining all available information. Furthermore, we provide a data model to facilitate storage and querying in a relational database. 

 

 

 

2.  Bitcoin transaction overview:

 

The bitcoin digital currency allows users to securely prove ownership of a portion of coins that cascade through the network as a chain of re-assigned ownershiptransactions over time.A transaction on the bitcoin network is a many-to-many function, executed by a user who has ownership to (potentially many) outputs of previous transactions; the user takes this owned value and writes ownership to (potentially many) output nodes (users, represented by addresses in the network).

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Twitter: Social Network Or News Medium?

An extensive research performed by the Korea Advanced Institute of Science and Technology
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Cluster your Twitter Data with R and k-means

Cluster your Twitter Data with R and k-means | Social Network Analysis #sna | Scoop.it

Hello everbody! Today  I want to show you how you can get deeper insights into your Twitter followers with the help of R

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A Zoomable Graph of the History of Philosophy

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Pay-it-forward cascades circle the globe via Facebook

Pay-it-forward cascades circle the globe via Facebook | Social Network Analysis #sna | Scoop.it
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Social Media Mining - free book on #SNA

Social Media Mining - free book on #SNA | Social Network Analysis #sna | Scoop.it
ukituki's insight:

Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.

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Beyond the name tag. Connecting people and knowledge at conferences

Beyond the name tag. Connecting people and knowledge at conferences | Social Network Analysis #sna | Scoop.it
""As knowledge-intensive social events, conferences open up a space in which people and organizations can share and generate knowledge, intensify their existing cooperation activities and establish new contacts. By bringing together people
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Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks

Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks | 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.
ukituki's insight:

Recent research has focused on the monitoring of global–scale online data for improved detection of epidemics, mood patterns, movements in the stock market political revolutions, box-office revenues, consumer behaviour and many other important phenomena. However, privacy considerations and the sheer scale of data available online are quickly making global monitoring infeasible, and existing methods do not take full advantage of local network structure to identify key nodes for monitoring. Here, we develop a model of the contagious spread of information in a global-scale, publicly-articulated social network and show that a simple method can yield not just early detection, but advance warning of contagious outbreaks. In this method, we randomly choose a small fraction of nodes in the network and then we randomly choose a friend of each node to include in a group for local monitoring. Using six months of data from most of the full Twittersphere, we show that this friend group is more central in the network and it helps us to detect viral outbreaks of the use of novel hashtags about 7 days earlier than we could with an equal-sized randomly chosen group. Moreover, the method actually works better than expected due to network structure alone because highly central actors are both more active and exhibit increased diversity in the information they transmit to others. These results suggest that local monitoring is not just more efficient, but also more effective, and it may be applied to monitor contagious processes in global–scale networks.

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The Collaborative Organization: How to Make Employee Networks Really Work | MIT Sloan Management Review

The Collaborative Organization: How to Make Employee Networks Really Work | MIT Sloan Management Review | Social Network Analysis #sna | Scoop.it
Once managers grasp the patterns of employee interactions, they can reduce network inefficiencies.
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Network data

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Understanding Types of Users on Twitter

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The Bechdel Test of Social Media

The Bechdel Test of Social Media | Social Network Analysis #sna | Scoop.it
The Bechdel test is a popular tool to analyze the role of women in movies, defining three conditions for a movie to pass the test: It contains two female characters Who talk to each other About something besides a man…
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Celebrity twitter followers by gender

Celebrity twitter followers by gender | Social Network Analysis #sna | Scoop.it
The most popular accounts on twitter have millions of followers, but what are their demographics like? Twitter doesn’t collect or release this kind of information, and even things like name and location are only voluntarily added to people’s profiles. Unlike Google+ … Continue reading →
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R, igraph and handsome graphs

ukituki's insight:

Slides from a presentation to the Chicago R User Group on Oct 3, 2012. It covers how to use R and Gephi to visualize a map of influence in the history of philosophy. 

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The Social Network of Foreign Ministries

The Social Network of Foreign Ministries | Social Network Analysis #sna | Scoop.it
I have often wondered if foreign ministries follow each other on twitter, and if so, do foreign ministries regard twitter as an important source of information. After all, by monitoring the Ukraini...
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Temporal networks with igraph and R (with 20 lines of code!)

The temporal evolution of the network yields to another perspective of social structure and, in some cases, aggregating the data in a time window might blur out important temporal structures on information diffusion, community or opinion formation, etc. Although many of the commercial and free Social Network Analysis software have tools to visualize static networks, there are no so many options out there for dynamical networks.

ukituki's insight:

 In this post I will show you how to render the network at each time step and how to encode all snapshots into a video file using the igraph package in R and ffmpeg. The idea is very simple

generate a number of snapshots of the network at different times using R and igraph, andthen put them together in a video file using ffmpeg.
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Algorithmic detection of specialization in online conversations

Algorithmic detection of specialization in online conversations | Social Network Analysis #sna | Scoop.it

Via Pierre Levy
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Network Research Centers - Social Network Analysis (SNA)

Network Research Centers - Social Network Analysis (SNA) | Social Network Analysis #sna | Scoop.it
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luiy's curator insight, June 5, 8:34 AM

An open wiki of Network Research Centers, originally curated by John Maloney and Raffaele Vacca. It includes a list and a map of centers. Please use the NRC Submit Form to add or adjust entries in the list.

 

The map can be edited by anyone in Google Maps.  Note: In most cases, the "Year Created" variable (year in which the center was created) is an estimate based on the publication date of the oldest center publication listed in the center website. Please correct it if you have more accurate information on a specific center.


To be added to the site/wiki access lists contact Colabria. This open Website and curated lists are supported by your donations. 

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User Interactions in Social Networks and their Implications

User Interactions in Social Networks and their Implications | Social Network Analysis #sna | Scoop.it
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Proxy Networks--Analyzing One Network To Reveal Another

Proxy Networks--Analyzing One Network To Reveal Another | Social Network Analysis #sna | Scoop.it
Proxy Networks--Analyzing One Network To Reveal Another
ukituki's insight:

Two books are linked if they were bought together at a major retailer on the web. I call these "buddy books". A link was drawn if either book of a pair listed the other as a buddy. The data made public by the retailer shows just the "best buddies" — the strongest ties. Other patterns may emerge with investigation of weaker ties. Amazon reveals only the top five or six books bought concurrently with a particular book. Seeing dozens of buddy books for each book would reveal some of the weaker ties and no doubt affect the structure of our network.

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luiy's curator insight, April 15, 6:28 PM

This article uses this network tie information to construct social networks of "buddy books". A lthough the actual political affiliation of each book purchaser is not known, the structure of the buddy book network shows that there are two clearly divided groups: a larger and morediffuse left-of-center readership, and a smaller and more closely tied right-of-centerreadership. Types or networks of readers linked to a specific author are also studied.