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Social Network Analysis #sna
Social Network Analysis
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“It Takes a Network”: The Rise and Fall of Social Network Analysi in U.S. Army Counterinsurgency Doctrine

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Origin of Peer Influence in Social Networks

Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends’ friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be.
DOI: http://dx.doi.org/10.1103/PhysRevLett.112.098702

Origin of Peer Influence in Social Networks
Phys. Rev. Lett. 112, 098702 – Published 6 March 2014
Flávio L. Pinheiro, Marta D. Santos, Francisco C. Santos, and Jorge M. Pacheco


Via Complexity Digest, Ashish Umre, Frédéric Amblard
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Eli Levine's curator insight, March 10, 2014 5:16 PM

Indeed, we are all interconnected in very profound and subtle ways, whether we accept it or not.


This one's for the Libertarians and conservatives out there, who don't seem to think that their actions effect the other, or that the other can effect them, or that the actions done onto the other will effect the actions that are done onto them by the other.

 

Kind of like how they blame the poor for being angry at the rich, after the poor produced the wealth that engorges the rich.

 

Silly people....

 

Think about it.

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Dirk Helbing: Rethinking Economics Based on Complexity Theory - YouTube

Dirk Helbing: Rethinking Economics Based on Complexity Theory Talk given at the Latsis Symposium 2012 "Economics on the Move" in Zurich, see http://www.multi...
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Social Media Analysis Reveals The Complexities Of Syrian Conflict | MIT Technology Review

Social Media Analysis Reveals The Complexities Of Syrian Conflict | MIT Technology Review | Social Network Analysis #sna | Scoop.it
Computer scientists have used the pattern of social media communication in Syria to reveal the structure of opposing forces in the civil war.
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Social networks for managers

Revision of Previous Show on SNA and Introduction to Tools The Language of Networks Introduction to Social Network Analysis/ Cases Tools for Analyzing social...

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june holley's curator insight, March 5, 2014 11:20 AM

Lots in here about social network mapping and analysis.

Liz Rykert's curator insight, March 6, 2014 12:58 PM

Thanks for this one June!

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Network Science Book by Albert Laszlo Barabasi

Network Science Book by Albert Laszlo Barabasi | Social Network Analysis #sna | Scoop.it
The power of network science, the beauty of network visualization.
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Harvesting and Analyzing Tweets | School of Data - Evidence is Power

Harvesting and Analyzing Tweets | School of Data - Evidence is Power | Social Network Analysis #sna | Scoop.it
Evidence is Power
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Marco Valli's curator insight, February 9, 2014 11:32 AM

Feels like a nice and complete tutorial! I'm definitely taking a closer look to ScraperWiki, but I'd like to be able to do the analysis in R/Python...maybe one day!

Premsankar Chakkingal's curator insight, February 10, 2014 7:43 PM

Tutorial to harvest tweets from Twitter using ScraperWiki and how to analyse them using social network analysis and  Gephi. 

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Introduction to Complex Systems: Patterns in Nature

This video provides a basic introduction to the science of complex systems, focusing on patterns in nature. (For more information on agent-based modeling, visit http://imaginationtoolbox.org ).


Via Lorien Pratt, Complexity Digest
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António F Fonseca's curator insight, February 1, 2014 4:50 AM

Agent based modeling still is the best tool to understand complex systems when mathematical modeling gets very complicated.

Liz Rykert's curator insight, February 10, 2014 7:25 PM

Always looking for good resources to introduce complexity science to others. This looks great. 

Ian Biggs, MAIPM, CPPE's curator insight, April 16, 2014 8:08 PM

I recently conducted a series of workshops on the subject of 'Complex Project Management - Navigating through the unknown'. This clip provides a great introduction to complex systems and for those interested in Complexity Science, this clip is worth 7:52 of your time.

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Twitter Trends Help Researchers Forecast Viral Memes

Twitter Trends Help Researchers Forecast Viral Memes | Social Network Analysis #sna | Scoop.it

What makes a meme— an idea, a phrase, an image—go viral? For starters, the meme must have broad appeal, so it can spread not just within communities of like-minded individuals but can leap from one community to the next. Researchers, by mining public Twitter data, have found that a meme's “virality” is often evident from the start. After only a few dozen tweets, a typical viral meme (as defined by tweets using a given hashtag) will already have caught on in numerous communities of Twitter users. In contrast, a meme destined to peter out will resonate in fewer groups.

 


Via Claudia Mihai
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june holley's curator insight, January 23, 2014 8:31 AM

Some important ideas here for people interested in change.

Premsankar Chakkingal's curator insight, January 30, 2014 8:58 AM

Forecasting the Future Twitter Trends in hashtags

Christian Verstraete's curator insight, February 3, 2014 4:48 AM

Twitter, what happens when things go viral?

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Libor Connections Form a Spider Network

Libor Connections Form a Spider Network | Social Network Analysis #sna | Scoop.it
ukituki's insight:
Libor: The Spider Network

The Libor manipulation scandal has ensnared at least 17 financial institutions and 22 individuals in a wide-ranging investigation spanning 11 countries and four continents. So far, it has netted at least $5 billion in penalties, with more on the way. Below, we've taken the most complete list of allegedly involved parties, compiled by WSJ reporters and editors, and mapped an extensive web of 298 reported connections that reveals the depth of the alleged conspiracy. Connections do not represent allegations of wrongdoing. The Journal has attempted to contact every institution and individual mentioned in this graphic. Their comments, if any, are included.

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The Potential of Social Network Analysis in Intelligence

The Potential of Social Network Analysis in Intelligence | Social Network Analysis #sna | Scoop.it
Within its limits, SNA can be applied to identify individuals or organizations within a network, generate new leads and simulate flows of information or money throughout a network.
ukituki's insight:

Like every analytic technique, SNA has great utility for the right question. Within its limits, SNA is unmatched and can be usefully applied to identify key individuals or organizations within a network, generate new leads and simulate the flows of information or money throughout a network.  SNA, however, remains just an answer, not the answer.  Used inappropriately or without a full understanding of the limits of the method and analysts will only be finding new and more technically sophisticated ways to fail.  That, then, is the primary job of the modern day analyst: making the judgment call of which techniques to use and when.  Equally as important as knowing when to use SNA is knowing when not to use it.

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Graph-based multimodal clustering for social event detection in lar...

Presentation by my colleague Giorgos Petkos of our paper at the Multimedia Modeling conference (MMM2014) in Dublin.
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Making sense of Big Data : mining Twitter names

Making sense of Big Data : mining Twitter names | Social Network Analysis #sna | Scoop.it

Millions of geo tweets in various languages, discussing anything from 'hey, I'm here' to finance, geopolitics or marketing. How do you make sense of them? 

ukituki's insight:

We’ve used name recognition (applied onomastics) to filter information and produce unique maps of the e-Diasporas. Where are the digitally connected Italian, Turkish and Russian today? They may be migrants, tourists, business travellers, student, visiting scientists…

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luiy's curator insight, December 21, 2013 11:51 AM

Our name recognition software can predict, given a person name : its cultural and linguistic classification, country of origin, gender and spelling variants.

 

Our onomastics blog presents a few examples of data visualizations, prepared using NamSor™ Onomastics software (NomTri™).

To know more about what we do, visit our website at http://namsor.com/ or email us at contact@namsor.com

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The Simple Rules of Social Contagion

The Simple Rules of Social Contagion | Social Network Analysis #sna | Scoop.it
It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.

Via Claudia Mihai
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Arjen ten Have's curator insight, March 12, 2014 8:21 AM

These are things we need to consider when we think about society.

Eli Levine's curator insight, March 12, 2014 2:53 PM

I've come to the conclusion that I am not going to spread like wildfire throughout the whole of the population.  My best bank is target who reads what I've got to write, so as to increase the chances that I'm able to do what I'm drawn to do.

 

Who knows if this will work.

 

But I'd rather try than do nothing; take the chance of failure rather than the guarantee of it.

 

Think about it.

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Netconomics: Novel Forecasting Techniques from the Combination of Big Data, Network Science and Economics

Netconomics: Novel Forecasting Techniques from the Combination of Big Data, Network Science and Economics | Social Network Analysis #sna | Scoop.it

The combination of the network theoretic approach with recently available abundant economic data leads to the development of novel analytic and computational tools for modelling and forecasting key economic indicators. The main idea is to introduce a topological component into the analysis, taking into account consistently all higher-order interactions. We present three basic methodologies to demonstrate different approaches to harness the resulting network gain. First, a multiple linear regression optimisation algorithm is used to generate a relational network between individual components of national balance of payment accounts. This model describes annual statistics with a high accuracy and delivers good forecasts for the majority of indicators. Second, an early-warning mechanism for global financial crises is presented, which combines network measures with standard economic indicators. From the analysis of the cross-border portfolio investment network of long-term debt securities, the proliferation of a wide range of over-the-counter-traded financial derivative products, such as credit default swaps, can be described in terms of gross-market values and notional outstanding amounts, which are associated with increased levels of market interdependence and systemic risk. Third, considering the flow-network of goods traded between G-20 economies, network statistics provide better proxies for key economic measures than conventional indicators. For example, it is shown that a country's gate-keeping potential, as a measure for local power, projects its annual change of GDP generally far better than the volume of its imports or exports.


Via Claudia Mihai
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Macro Connections | The MIT Media Lab

Macro Connections | The MIT Media Lab | Social Network Analysis #sna | Scoop.it
The Macro Connections Group at The MIT Media Lab works on Visualization, Cities, Networks, Economic Complexity and Cultural Production
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Thinking About Betweenness Centrality (UMA Social Networks) - YouTube

Understanding betweenesss centrality in social networks is very important, but can also be a bit tricky to calculate. This video offers a few social networks...

Via João Greno Brogueira
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A Brief Primer on Human Social Networks, or How to Keep $16 Billion In Your Pocket — Sonra Oku — Medium

A Brief Primer on Human Social Networks, or How to Keep $16 Billion In Your Pocket — Sonra Oku — Medium | Social Network Analysis #sna | Scoop.it
Over at The New York Times, Jenna Wortham wonders whether Facebook’s acquisition of Whatsapp points to a resurgence of small social…
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Twitter Data Mining Round Up

Twitter Data Mining Round Up | Social Network Analysis #sna | Scoop.it
Since the release of Mining the Social Web, 2E in late October of last year, I have mostly focused on creating supplemental content that focused on Twitter data. This seemed like a natural starting...
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Socially Mapping the Isle of Wight - @onthewight Twitter ESP

Socially Mapping the Isle of Wight - @onthewight Twitter ESP | Social Network Analysis #sna | Scoop.it
ukituki's insight:

The view aims to map out accounts that are followed by 10 or more people from a sample of about 200 or so followers of @onthewight. The network is layed out according to a force directed layout algorithm with a dash of aesthetic tweaking; nodes are coloured based on community grouping as identified using the Gephi modularity statistic. Which has it’s issues, but it’s a start. The nodes are sized in the first case according to PageRank.

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Puppies! Now that I’ve got your attention, complexity theory

Animal behavior isn't complicated, but it is complex. Nicolas Perony studies how individual animals -- be they Scottish Terriers, bats or meerkats -- follow simple rules that, collectively, create larger patterns of behavior. And how this complexity born of simplicity can help them adapt to new circumstances, as they arise.

 

http://www.ted.com/talks/nicolas_perony_puppies_now_that_i_ve_got_your_attention_complexity_theory.html


Via Complexity Digest, Jorge Louçã, NESS, António F Fonseca
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António F Fonseca's curator insight, February 4, 2014 9:40 AM

The guy seems to be confessing some obscure personal sin but the talk is very interesting.

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Moneyball for Academics: network analysis methods for predicting the future success of papers and researchers.

Moneyball for Academics: network analysis methods for predicting the future success of papers and researchers. | Social Network Analysis #sna | Scoop.it

Drawing from a combination of network analysis measurements, Erik Brynjolfsson and Shachar Reichman present methods from their research on predicting the future success of researchers.

ukituki's insight:

We analyzed the combination of the publications network (i.e. citation network), the authors’ social network (i.e. co-authorship network) and the links that connect the 2 networks which generate a dual-network structure (see figure 1). Using data from Thomson-Reuters Web of Knowledge, we created a set of yearly snapshots of the papers-authors dual-networks from 1975 to 2012 on over 700,000 papers published in management, information systems and operations research journals. For each network snapshot we computed common centrality measures of it nodes as part of the variables in our models.

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Sudhakar B.S.'s curator insight, January 18, 2014 6:52 AM

Interesting insights, page ranks can play a key role too. Similar logic can be applied to identify brand advocates and influencers for Brands

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Epidemics on social networks

Since its first formulations almost a century ago, mathematical models for disease spreading contributed to understand, evaluate and control the epidemic processes.They promoted a dramatic change in how epidemiologists thought of the propagation of infectious diseases.In the last decade, when the traditional epidemiological models seemed to be exhausted, new types of models were developed.These new models incorporated concepts from graph theory to describe and model the underlying social structure.Many of these works merely produced a more detailed extension of the previous results, but some others triggered a completely new paradigm in the mathematical study of epidemic processes. In this review, we will introduce the basic concepts of epidemiology, epidemic modeling and networks, to finally provide a brief description of the most relevant results in the field.

 

Epidemics on social networks
Marcelo N. Kuperman

http://arxiv.org/abs/1312.3838


Via Complexity Digest
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António F Fonseca's curator insight, January 9, 2014 5:10 AM

A good review about epidemic models in social networks, SIS, SIR, etc ...

Marco Valli's curator insight, January 9, 2014 9:08 AM

Basics of SIS/SIR models of spreading epidemics, and their relations to social networks.

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Analysis of first co authored presentation

Results of analyzing first co-authored presentation on slideshare using advanced analysis tools
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State of the Net 2012 – People Tweet Tacit Knowledge

ukituki's insight:

Dave Snowden challenges prevailing wisdom on work, organizational practices, and the future of the Internet.


https://www.youtube.com/watch?v=uvkLR3pa5QI&list=WLy6Gm1F4KHfF8Eb_pJS12nw0eTIjgxBAg ;



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