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Influence et contagion
L'influence et la contagion dans la cyberculture
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How #Network Science Is Changing Our Understanding of #Law | #SNA #patterns

How #Network Science Is Changing Our Understanding of #Law | #SNA #patterns | Influence et contagion | Scoop.it
The first network analysis of the entire body of European Community legislation reveals the pattern of links between laws and their resilience to change.

Via Complexity Digest
luiy's insight:

One of the more fascinating areas of science that has emerged in recent years is the study of networks and their application to everyday life. It turns out that many important properties of our world are governed by networks with very specific properties.

 

These networks are not random by any means. Instead, they are often connected in the now famous small world pattern in which any part of the network can be reached in a relatively small number of steps. These kinds of networks lie behind many natural phenomena such as earthquakes, epidemics and forest fires and are equally ubiquitous in social phenomena such as the spread of fashions, languages, and even wars.

 

So it should come as no surprise that the same kind of network should exist in the legal world. Today, Marios Koniaris and pals at the National Technical University of Athens in Greece show that the network of links between laws follows exactly the same pattern. They say their network approach provides a unique insight into the nature of the law, the way it has emerged and how changes may influence it in the future.

 

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GALLERY SocioPatterns project. SocioPatterns.org | #SNA #datascience

GALLERY SocioPatterns project. SocioPatterns.org | #SNA #datascience | Influence et contagion | Scoop.it
A gallery that offers a collection of visualizations, pictures, movies and other media created and/or recorded in the context of the SocioPatterns project.
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Dynamical Contact Patterns in a Primary School

 

This movie represents the dynamical contacts network measured during one day of activity in a primary school. Nodes represent individuals, and edges indicate face-to-face contacts. Every frame shows the contact network over a time window of 20 minutes. Nodes are arranged in groups that correspond to the school classes, with the teacher node at the center. Nodes are color-coded according to the grade  and teachers are shown in black. This movie is included in the supplementary information of our PLoS ONE paper. The network visualization was created by Alain Barrat and André Panisson using Gephi. The cumulative social network of interaction is available from the corresponding dataset page.

 

- See more at: http://www.sociopatterns.org/gallery/#sthash.ae3X56fs.dpuf

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News Information Flow Tracking, Yay! (NIFTY) : System for large scale real-time tracking of #memes | #datascience #algorithms

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

PLOS ONE #Complex systems articles | #ABM #netwoks #research | Influence et contagion | 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
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Study suggests that social-network support for causes might be only a click deep | #SNA #donations

Study suggests that social-network support for causes might be only a click deep | #SNA #donations | Influence et contagion | Scoop.it

Social media may fuel unprecedented civic engagement.


Via Jesse Soininen
luiy's insight:

Lewis and colleagues analyzed the donation and recruitment activity of more than 1 million members of the Save Darfur Cause between May 2007 and January 2010. About 80 percent of the members had been recruited by other members and about 20 percent had joined independently.

 

Of these 1 million-plus members, 99.76 percent never donated any money and 72.19 percent never recruited anyone else.

 

The Save Darfur Cause on Facebook raised only about $100,000. While the average donation amounts were similar to more traditional fundraising methods ($29.06), the donation rate was much smaller: 0.24 percent. Compare that to mail solicitations which typically yield donation rates of 2 to 8 percent. The larger Save Darfur campaign, the researchers note, raised more than $1 million through direct-mail contributions in fiscal year 2008 alone.

 

Interestingly, those that had joined the Facebook cause independently were both more likely to donate and to recruit.

Social and financial contributions, though rare on both counts, also tended to go hand-in-hand. Those individuals that did recruit were nearly four times as likely as non-recruiters to donate. And donors were more than twice as likely as non-donors to recruit.

The data contained no demographic information on the cause's members. Nor, the researchers write, could they estimate "the personal significance of [the joining] gesture to participants or the symbolic impact of the movement to onlookers.

 

"It is possible," they add, "that the individuals in our data set contributed to Save Darfur in other meaningful but unobserved ways."

 

Still, Lewis and colleagues believe the study gives some valuable insights into collective action in a digital age.



Read more at: http://phys.org/news/2014-03-social-network-click-deep.html#jCp

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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 | Influence et contagion | 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 ...
luiy's insight:

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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The Structure of Online Diffusion Networks I #adoptions #patterns

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In order to identify generic features of online diffusion structure, we study seven diverse examples comprising millions of individual adopters. As opposed to biological contagion, our domain of interest comprises the diffusion of adoptions, where “adop- tion” implies a deliberate action on the part of the adopting individual. In particular, we do not consider mere exposure to an idea or product to constitute adoption. Conta- gious processes such as email viruses, which benefit from accidental or unintentional transmission are therefore excluded from consideration.


Although restricted in this manner, the range of applications that we consider is broad. The seven studies described below draw on different sources of data, were recorded using different technical mechanisms over different timescales, and varied widely in terms of the costliness of an adoption. This variety is important to our con- clusions, as while each individual study no doubt suffers from systematic biases arising from the particular choice of data and methods, collectively they are unlikely to all ex- hibit the same systematic biases. To the extent that we observe consistent patterns across all examples, we expect that our findings should be broadly applicable to other examples of online—and possibly offline—diffusion as well.


The remainder of this paper proceeds as follows. After reviewing the diffusion liter- ature in Section 2, in Section 3 we describe in detail the seven domains we investigate. We present our main results in Section 4, showing that not only are most cascades small and shallow, but also that most adoptions lie in such cascades. In particular, it is rare for adoptions to result from chains of referrals. Finally, in Section 5 we discuss the implications of these results for diffusion models, as well as the apparent discord between our results and the prevalence of popular products, such as Facebook and Gmail, whose success is often attributed to viral propagation. 

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Selection and Influence in Cultural Dynamics

luiy's insight:

One of the fundamental principles driving diversity or homogeneity in domains such as cultural differentiation, political affiliation, and product adoption is the tension between two forces: influence (the tendency of people to become similar to others they interact with) and selection (the tendency to be affected most by the behavior of others who are already similar). Influence tends to promote homogeneity within a society, while selection frequently causes fragmentation. When both forces are in effect simultaneously, it becomes an interesting question to analyze which societal outcomes should be expected. 


In order to study the joint effects of these forces more formally, we analyze a natural model built upon active lines of work in political opinion formation, cultural diversity, and language evolution. Our model posits an arbitrary graph structure describing which "types" of people can influence one another: this captures effects based on the fact that people are only influenced by sufficiently similar interaction partners. In a generalization of the model, we introduce another graph structure describing which types of people even so much as come in contact with each other. These restrictions on interaction patterns can significantly alter the dynamics of the process at the population level. 


For the basic version of the model, in which all individuals come in contact with all others, we achieve an essentially complete characterization of (stable) equilibrium outcomes and prove convergence from all starting states. For the other extreme case, in which individuals only come in contact with others who have the potential to influence them, the underlying process is significantly more complicated; nevertheless we present an analysis for certain graph structures.

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Dynamical Contact #Patterns in a Primary School | #SNA #learning

luiy's insight:

This movie represents the dynamical evolution of the contacts during the first day of a deployment of the SocioPatterns sensing platform, see sociopatterns.org. Each dot represents an individual, and an edge is drawn when a contact between two individuals occurs. Only contacts lasting at least 40 s are retained. Each frame corresponds to an aggregation of the contact network over a time window of 20 mn, and successive frames correspond to aggregation time windows shifted by 10 s; the movie is then built using 20 frames per second. Nodes are disposed in circles corresponding to the various classes, with the teacher at the center, and color-coded according to the grade (teachers are shown in black).

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Mapping the Information #Economy: A Tale of Five Industries | #patterns #SNA

Mapping the Information #Economy: A Tale of Five Industries | #patterns #SNA | Influence et contagion | Scoop.it

At Box, we constantly measure customers’ engagement with our product to understand how to enhance user experience and help businesses be more productive and collaborative. With 25 million users at 225,000 businesses interacting with content 2.5 billion times quarterly, we have a unique vantage point on how enterprises in nearly every sector leverage the cloud.

luiy's insight:

But what, if anything, can the patterns in how businesses share information tell us about how they operate more generally? Zooming out, what might these patterns signal about entire industries and their relative preparedness for adapting to an increasingly information-driven economy? We’re entering an era where a company’s competitiveness is determined by its return on information – how democratized its access is, how fast it moves, and how quickly it can be updated and leveraged to generate value.

 

For our first Information Economy Report, we started by visually mapping the flow of information within customer organizations. Every red node represents an employee, every blue node an external collaborator, and every line a transfer of content, with thicker lines indicating more frequent sharing. The results were beautiful, and also telling.

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The six types of Twitter conversations | #patterns #archetypes

The six types of Twitter conversations | #patterns #archetypes | Influence et contagion | Scoop.it
Have you ever wondered what a Twitter conversation looks like from 10,000 feet?

Via Pierre Levy
luiy's insight:

Have you ever wondered what a Twitter conversation looks like from 10,000 feet? A new report from the Pew Research Center, in association with the Social Media Research Foundation, provides an aerial view of the social media network. By analyzing many thousands of Twitter conversations, we identified six different conversational archetypes. Our infographic describes each type of conversation network and an explanation of how it is shaped by the topic being discussed and the people driving the conversation.

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Kamian's curator insight, March 26, 2014 11:57 PM

Me encantan estas clasificaciones, ayudan a comprender y diferenciar rapidamente las diferentes dinámicas sociales y arquitecturas que se van conformando en las redes sociales.

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#Truthy : Information diffusion research | #political #memes #patterns #SNA

#Truthy : Information diffusion research | #political #memes #patterns #SNA | Influence et contagion | Scoop.it
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luiy's curator insight, March 21, 2014 6:50 PM

Information diffusion research at Indiana University

 

Truthy is a research project that helps you understand how communication spreads on Twitter. 

 

We currently focus on tweets about politics, social movements and news.

 

 

Political Topics

Interactive visualizations of U.S. political conversation on Twitter :

 

- How does sentiment change over time in response to political events?

- What is most popular over time?

- Who are the most influential users?

- How does information spread in the social network?

 

 

Sentiment Timeline

- How does sentiment change over time in response to political events?

 

 

Gallery Descriptions of interesting memes:  http://truthy.indiana.edu/gallery

 

 

Meme Patterns:

What other memes are related to this one?  http://truthy.indiana.edu/memedetail?id=783&resmin=45&theme_id=4

 

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Time varying networks and the weakness of strong ties | #patterns #rumor #SNA

Time varying networks and the weakness of strong ties | #patterns #rumor #SNA | Influence et contagion | Scoop.it

In most social and information systems the activity of agents generates rapidly evolving time-varying networks. The temporal variation in networks' connectivity patterns and the ongoing dynamic processes are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset and find a simple statistical law that characterize the temporal evolution of users' egocentric networks. We encode this observation in a reinforcement process defining a time-varying network model that exhibits the emergence of strong and weak ties. We study the effect of time-varying and heterogeneous interactions on the classic rumour spreading model in both synthetic, and real-world networks. We observe that strong ties severely inhibit information diffusion by confining the spreading process among agents with recurrent communication patterns. This provides the counterintuitive evidence that strong ties may have a negative role in the spreading of information across networks.

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Mathematical Formulation of Multilayer Networks I #SNA #complexity #patterns

Mathematical Formulation of Multilayer Networks I #SNA #complexity #patterns | Influence et contagion | 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 NESS
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#BigData & Causal Inference. Measuring and Propagation #Influence in Networks | #Patterns

Sinan Aral is a leading expert on Social Networks, Social Media and Digital Strategy. He is an Assistant Professor and Microsoft Faculty Fellow at the NYU St...
luiy's insight:

Among his several lines of research, Dr. Aral has done notable work studying how information diffusion in massive online social networks influences demand patterns, consumer e-commerce behaviors and word of mouth marketing. Sinan is a Phi Beta Kappa graduate of Northwestern University, holds masters degrees from the London School of Economics and Harvard University, and received his PhD from MIT. You can find him on Twitter @sinanaral.

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The Many Faces of Influence [infographic]

The Many Faces of Influence [infographic] | Influence et contagion | Scoop.it

Inspired by the rich variety of influencer types our customers discover, we looked for patterns and identified 10 key archetypes. Among these influencer profiles are:

the Authority, the influencer who is expert in connecting topic areas and can package insights into a meaningful bundle for his audience;the Insider, who finds alliances to build the market story he needs to tell and pushes the industry forward; andthe Agitator, who always looks for ways to stir the pot and push conversations to new heights. 

Filled with fun facts and tips about what motivates the different types of online influencers,The Many Faces of Influence infographic is a simple guide to understanding how to be a part of their community and knowing the best ways to engage them.

Explore this inforgraphic to learn more about The Many Faces of Influence...


Via Lauren Moss
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AreaDining's curator insight, April 3, 2013 12:44 PM

Influencers are the lifeblood to social media success and credibility