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Predicting Group Evolution in the Social Network

Groups - social communities are important components of entire societies, analysed by means of the social network concept. Their immanent feature is continuous evolution over time. If we know how groups in the social network has evolved we can use this information and try to predict the next step in the given group evolution. In the paper, a new aproach for group evolution prediction is presented and examined. Experimental studies on four evolving social networks revealed that (i) the prediction based on the simple input features may be very accurate, (ii) some classifiers are more precise than the others and (iii) parameters of the group evolution extracion method significantly influence the prediction quality
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Social Network Analysis #sna
Social Network Analysis
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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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Does the internet promote fairness of income distribution? (w/ Video) | #SNA #patterns

Does the internet promote fairness of income distribution? (w/ Video) | #SNA #patterns | Social Network Analysis #sna | Scoop.it
(Phys.org) —The question of how an economic system should be structured in order to best promote fairness and equality is one of the most debated subjects of all time. By approaching the complexities of this question from the field of network science, researchers from MIT and other institutions have ...

Via luiy
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luiy's curator insight, February 10, 10:52 AM

In their study, the researchers constructed a model in which individuals can earn income in two ways: by producing content or by distributing the content produced by others. A system in which more income is earned by production than by distribution is labeled as meritocratic, while one in which more income is earned by distribution is called topocratic. Importantly, the income earned by distribution depends not on what an individual produces but rather on an individual's position in the network.

 

Using this simple model, the researchers showed that the connectivity of the network determines whether the income is earned in a meritocratic or topocratic manner: densely connected networks are more meritocratic, while sparsely connected networks are more topocratic.

 

The difference makes sense, since individuals in densely connected networks can sell what they produce directly to others, and therefore do not need to share much of their proceedings with middlemen. On the other hand, in sparsely connected networks, individuals do not have direct connections with buyers and must rely on middlemen to help them connect.


Read more at: http://phys.org/news/2014-01-internet-fairness-income-video.html#jCp

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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Social Network Analysis #sna | Scoop.it

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luiy's curator insight, March 27, 10:44 AM

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

António F Fonseca's curator insight, April 2, 3:01 AM

Another paper about popularity prediction.

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Think Link: Network Insights with No Programming Skills

Think Link: Network Insights with No Programming Skills | Social Network Analysis #sna | Scoop.it
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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, 2:16 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, 8:20 AM

Lots in here about social network mapping and analysis.

Liz Rykert's curator insight, March 6, 9:58 AM

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, 8:32 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, 4:43 PM

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

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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, 3:28 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.

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Modeling Mutual Influence Between Social Actions and Social Ties

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PLOS ONE #Complex systems articles | #ABM #netwoks #research

PLOS ONE #Complex systems articles | #ABM #netwoks #research | 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.


Via Bryan Knowles, Bernard Ryefield, Luciana Viter, Roger D. Jones, PhD, luiy
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Who’s Who Among VC Partners: An Algorithmic Take on Portfolio Strength, Network Centrality and Happiness

Who’s Who Among VC Partners: An Algorithmic Take on Portfolio Strength, Network Centrality and Happiness | Social Network Analysis #sna | Scoop.it
A network analysis of the top investors serving on the boards of Tech IPO Pipeline companies helps us start to answer the question: Who are some of the best VCs out there? (Interactive network graph included)
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Analytics Art: NBA passing

Analytics Art: NBA passing | Social Network Analysis #sna | Scoop.it
By Andrew Bergmann, for NBA.com Here’s a look at how starters on all 30 NBA teams share the basketball. (Click graphic to expand) The thickness of the gray lines on the accompanying chart represents the average number of passes per game between two players.
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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, 5:21 AM

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

Eli Levine's curator insight, March 12, 11:53 AM

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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