Social Network Analysis #sna
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Social Network Analysis #sna
Social Network Analysis
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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, FAIPM, 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
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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 Hidden Geometry of Complex, Network-Driven Contagion Phenomena

The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes. The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns, if conventional geographic distance is replaced by a probabilistically motivated effective distance. In the context of global, air-traffic–mediated epidemics, we show that effective distance reliably predicts disease arrival times. Even if epidemiological parameters are unknown, the method can still deliver relative arrival times. The approach can also identify the spatial origin of spreading processes and successfully be applied to data of the worldwide 2009 H1N1 influenza pandemic and 2003 SARS epidemic.

 

The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
Dirk Brockmann, Dirk Helbing

Science 13 December 2013:
Vol. 342 no. 6164 pp. 1337-1342
http://dx.doi.org/10.1126/science.1245200


Via Complexity Digest
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ComplexInsight's curator insight, December 31, 2013 3:59 AM

This is an awesome insight that needs tested across other datasets to find out how universal it is. Good paper.

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Charts of economic development: a fantastic journey

Charts of economic development: a fantastic journey | Social Network Analysis #sna | Scoop.it

Venezuelan economist Ricardo Hausmann and Chilean physicist César Hidalgo, in a joint effort of Harvard University and the Massachutes Institute of Technology MIT, draw a new world map of economic adventure, and suggest the Earth may not be flat.


Via Claudia Mihai, ukituki
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Mathematical Formulation of Multilayer Networks

Mathematical Formulation of Multilayer Networks | Social Network Analysis #sna | Scoop.it

Describing a social network based on a particular type of human social interaction, say, Facebook, is conceptually simple: a set of nodes representing the people involved in such a network, linked by their Facebook connections. But, what kind of network structure would one have if all modes of social interactions between the same people are taken into account and if one mode of interaction can influence another? Here, the notion of a “multiplex” network becomes necessary. Indeed, the scientific interest in multiplex networks has recently seen a surge. However, a fundamental scientific language that can be used consistently and broadly across the many disciplines that are involved in complex systems research was still missing. This absence is a major obstacle to further progress in this topical area of current interest. In this paper, we develop such a language, employing the concept of tensors that is widely used to describe a multitude of degrees of freedom associated with a single entity.

Our tensorial formalism provides a unified framework that makes it possible to describe both traditional “monoplex” (i.e., single-type links) and multiplex networks. Each type of interaction between the nodes is described by a single-layer network. The different modes of interaction are then described by different layers of networks. But, a node from one layer can be linked to another node in any other layer, leading to “cross talks” between the layers. High-dimensional tensors naturally capture such multidimensional patterns of connectivity. Having first developed a rigorous tensorial definition of such multilayer structures, we have also used it to generalize the many important diagnostic concepts previously known only to traditional monoplex networks, including degree centrality, clustering coefficients, and modularity.

We think that the conceptual simplicity and the fundamental rigor of our formalism will power the further development of our understanding of multiplex networks.

 


Via Claudia Mihai
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Communities, roles, and informational organigrams in directed networks: the Twitter network of the UK riots.

Communities, roles, and informational organigrams in directed networks: the Twitter network of the UK riots. | Social Network Analysis #sna | Scoop.it
#SNA: Communities, roles, and informational organigrams in directed networks: the Twitter network of the UK riots. By Mariano Beguerisse-Díaz, Guillermo Garduño-Hernández, Borislav Vangelov, Sophia N....
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Why Being The Most Connected Is A Vanity Metric

Why Being The Most Connected Is A Vanity Metric | Social Network Analysis #sna | Scoop.it
Research shows that being the most connected person is not an effective way to build your network. The single best strategy is one that almost no one talks about.
ukituki's insight:

As you can see in the chart, the further to the right you go toward a closed network, the more you’ll repeatedly hear the same ideas, which reaffirm what you already believe. The further left you go toward an open network, the more you’ll be exposed to new ideas. People to the left are significantly more successful than those to the right.

 

 

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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
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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

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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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Social network analysis for journalists using the Twitter API - YouTube

In an age of social media, social network analysis (SNA) is becoming a promising technique for the digital journalist's toolkit.
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Identifying influential edges in a directed network: big events, upsets and non-transitivity

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In this paper, we propose methods that identify influential edges in a network. The paper uses the proposed methods to analyse two networks from disparate applications—win–loss data of teams competing in the 2011 season of NCAA Football Bowl Subdivision and the 2001 Enron employee email dataset. Several edge measures are proposed. The first set of measures adapt node measures to the context of analysing the importance of edges. The second set include measuring betweenness, rank sensitivity to perturbations in the network and the sensitivity of the ranking as measured by a linear programme formulation. The methods are applied to the aforementioned networks and the benefits and appropriateness of the methods are discussed and contrasted.

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Early-warning signals of topological collapse in interbank networks

Early-warning signals of topological collapse in interbank networks | Social Network Analysis #sna | Scoop.it

The financial crisis clearly illustrated the importance of characterizing the level of ‘systemic’ risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998–2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear – but unpredictable – signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.


Via Claudia Mihai
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Efficient discovery of overlapping communities in massive networks

Efficient discovery of overlapping communities in massive networks | Social Network Analysis #sna | Scoop.it

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.


Via Claudia Mihai
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ComplexInsight's curator insight, December 31, 2013 4:02 AM

Network visualization tools like Gephi and analysis tools like SNAP are becoming essential components in understanding, mapping and comprehending inter-relating networks and network processes. This is a good paper that gives insight into appliying networking analysis tools to identify otherwise hidden community structures in apparhently disconnected or partially connected sets which will be hugely important in large scale network analysis.

Investors Europe Stock Brokers's curator insight, September 1, 2014 2:14 AM

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You're here because of a robot

You're here because of a robot | Social Network Analysis #sna | Scoop.it

Note: This post is co-written with Piotr Sapieżyński Is it possible for a small computer science course to exert measurable influence (trending topics) on Twitter..

ukituki's insight:

Is it possible for a small computer science course to exert measurable influence (trending topics) on Twitter, a massive social network with hundreds of millions of users? The surprising answer to that question is “yes”. That’s exactly what we did this year, using simple Python scripts and the Twitter API. Below we explain why & how + some of our findings along the way.

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