Influence et cont...
Follow
Find
3.1K views | +1 today
Influence et contagion
Trends sur l'influence et la contagion dans la cyberculture
Curated by luiy
Your new post is loading...
Your new post is loading...
Rescooped by luiy from Social Network Analysis Applications
Scoop.it!

The Network Secrets of Great #Change #Agents | #SNA #influence

The Network Secrets of Great #Change #Agents | #SNA #influence | Influence et contagion | Scoop.it
Business management magazine, blogs, case studies, articles, books, and webinars from Harvard Business Review, addressing today's topics and challenges in business management.

Via Premsankar Chakkingal
luiy's insight:

In tracking 68 of these initiatives for one year after their inception, we discovered some striking predictors of change agents’ success. The short story is that their personal networks—their relationships with colleagues—were critical. More specifically, we found that:

 

1. Change agents who were central in the organization’s informal network had a clear advantage, regardless of their position in the formal hierarchy.

 

2. People who bridged disconnected groups and individuals were more effective at implementing dramatic reforms, while those with cohesive networks were better at instituting minor changes.

 

3. Being close to “fence-sitters,” who were ambivalent about a change, was always beneficial. But close relationships with resisters were a double-edged sword: Such ties helped change agents push through minor initiatives but hindered major change attempts.

 

more...
Premsankar Chakkingal's curator insight, January 31, 9:54 PM
Change is hard, especially at large organizations. But some leaders do succeed at transforming their workplaces. How? The secret lies in how they understand and mobilize their informal networks:
Scooped by luiy
Scoop.it!

Creating Social #Contagion Through #Viral Product #Design: A Randomized Trial of Peer #Influence in Networks

Creating Social #Contagion Through #Viral Product #Design: A Randomized Trial of Peer #Influence in Networks | Influence et contagion | Scoop.it
luiy's insight:

We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. To econometrically identify the effectiveness of different viral features in creating social contagion, we designed and conducted a randomized field experiment involving the 1.4 million friends of 9,687 experimental users on Facebook.com. We find that viral features generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral features generate a 246% increase in peer influence and social contagion, whereas adding active-personalized viral features generate only an additional 98% increase. Although active-personalized viral messages are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often, generating more total peer adoption in the network. Our work provides a model for how randomized trials can identify peer influence in social networks.


Article in:  http://icos.umich.edu/sites/icos6.cms.si.umich.edu/files/lectures/VPDFinal1110.pdf

more...
No comment yet.
Scooped by luiy
Scoop.it!

Twitter Trends Help Researchers Forecast Viral #Memes | #SNA #contagion

Twitter Trends Help Researchers Forecast Viral #Memes | #SNA #contagion | Influence et contagion | Scoop.it
Researchers are forecasting which memes will spread far and wide
luiy's insight:

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.

 

“We didn't expect to see that the viral memes were going to behave very differently from nonviral memes at their beginnings,” says Lilian Weng, a graduate student in informatics at Indiana University Bloomington. Those differences allowed Weng and her colleagues to forecast memes that would go viral with an accuracy of better than 60 percent, the team reported in a 2013 study.

more...
No comment yet.
Scooped by luiy
Scoop.it!

Modeling #Emotion #Influence from Images in Social Networks | #SNA

Modeling #Emotion #Influence from Images in Social Networks | #SNA | Influence et contagion | Scoop.it
luiy's insight:

Images become an important and prevalent way to express users' activities, opinions and emotions. In a social network, individual emotions may be influenced by others, in particular by close friends. We focus on understanding how users embed emotions into the images they uploaded to the social websites and how social influence plays a role in changing users' emotions. We first verify the existence of emotion influence in the image networks, and then propose a probabilistic factor graph based emotion influence model to answer the questions of "who influences whom". Employing a real network from Flickr as experimental data, we study the effectiveness of factors in the proposed model with in-depth data analysis. Our experiments also show that our model, by incorporating the emotion influence, can significantly improve the accuracy (+5%) for predicting emotions from images. Finally, a case study is used as the anecdotal evidence to further demonstrate the effectiveness of the proposed model.

more...
No comment yet.
Rescooped by luiy from SNA - Social Network Analysis ... and more.
Scoop.it!

“Follow the Leader”: A #Centrality Guided #Clustering and Its Application to #SNA

“Follow the Leader”: A #Centrality Guided #Clustering and Its Application to #SNA | Influence et contagion | Scoop.it

"Within graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided clustering (CGC) is proposed in this paper. Different from traditional clustering methods which usually choose the initial center of a cluster randomly, the CGC clustering algorithm starts from a “LEADER”—a vertex with the highest centrality score—and a new “member” is added into the same cluster as the “LEADER” when some criterion is satisfied. The CGC algorithm also supports overlapping membership. Experiments on three benchmark social network data sets are presented and the results indicate that the proposed CGC algorithm works well in social network clustering."


Via João Greno Brogueira
luiy's insight:

In this work, a novel hierarchical clustering algorithm is proposed for social network clustering. Traditional clustering methods, such as -means, usually choose clustering centers randomly, and the hierarchical clustering algorithms usually start from two elements with shortest distance. Different from these methods, this work chooses the vertex with highest centrality score as the starting point. If one does some analysis on social network datasets, one may notice that in each community, there is usually some member (or leader) who plays a key role in that community. In fact, centrality is an important concept [13] within social network analysis. High centrality scores identify members with the greatest structural importance in a network and these members are expected to play key roles in the network. Based on this observation, this work proposes to start clustering from the member with highest centrality score. That is, a group is formed starting from its “leader,” and a new “member” is added into an existing group based on its total relation with the group. The main procedure is as follows. Choose the vertex with the highest centrality score which is not included in any existing group yet and call this vertex a “LEADER.” A new group is created with this “LEADER.” Repeatedly add one vertex to an existing group if the following criterion is satisfied: the density of the newly extended group is above a given threshold.

more...
No comment yet.
Scooped by luiy
Scoop.it!

The Structure of Online Diffusion Networks I #adoptions #patterns

luiy's insight:

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. 

more...
No comment yet.
Scooped by luiy
Scoop.it!

Onalytica: #BigData Influencers | #SNA #pagerank

Onalytica: #BigData Influencers | #SNA #pagerank | Influence et contagion | Scoop.it
luiy's insight:

The latest Onalytica ranking of Big Data influencers on Twitter is now available. The top 200 influencers are listed below.

 

Methodology: We took the tweets for the last 6 months containing the hashtag #BigData and treated mentions of other tweeters as a link to these. This initial network was made up of 150,486 nodes. After removing smaller, isolated components of the network we calculated mathematical PageRank, which is used to rank the list.

 

The methodology is different from our previous Big Data ranking which was based on Betweenness Centrality. This methodology weights connectors higher, but we have found that PageRank more effectively highlight the real influencers in the network.

 

The new entries in the top 200 are more due to people moving from below the 200 water mark to above because they are drawing more attention in the context of Big Data, than due to changes in the methodology.

more...
No comment yet.
Scooped by luiy
Scoop.it!

There Are No Super #Influencers: The Reality about Influencers from the world of #NetworkScience

There Are No Super #Influencers: The Reality about Influencers from the world of #NetworkScience | Influence et contagion | Scoop.it
The influence of influencers is overhyped. We all want to believe that there are these super-hero influencers that can make dramatic changes to organizations, countries, and societies. The idea has...
luiy's insight:

The idea has been spread in pop-culture in books like Malcolm Gladwell’s the Tipping Point. Recent developments in Network Sciencehave shown that our understanding of influencers – the super-connected individuals in our organizations and society – is more or less wrong.

 

So what is the truth behind influencers? The science is still figuring it out, but here is what we have learned so far.

 

It’s all about Micro-Influencers

The super-connected influencer do not exist, instead there are micro-influencers – those that have slightly more influence than the rest of the population influencing those around them to spread their ideas and messages about certain topics. (I would consider my friend Andrew a micro-influencer, he got our whole group of friends drinking high-quality craft beer after he himself jumped into the cult of American craft beer drinking).

 

We use to think that the human social network was constructed like our airport network (also called scale-free networks), there are hubs in which most traffic can get to most places, thus have huge influence on the flow of information.

 

The truth is that there are no Chicago O’Hare, or London Heathrow individuals.  Why? Because the human network does not work like the airport transportation network. The human capacity to manage relationships is finite. Unlike our major airports, we cannot just construct another terminal in ourselves to deal with more traffic. We have a limited number of relationships we can actively manage and the reach of our direct influence is limited by the relationships we manage.

 

The average number of friends people have on Facebook is around 200 – but there are some Facebook users who have 2000 friends (the max for an individual account), which is only 1 magnitude greater, not 10 or 20 times greater like we would expect if our human networks were more like airports: like the difference between Colorado Springs Airport traffic and Chicago O’Hare.

 

more...
Claude Emond's curator insight, January 16, 3:54 PM

very interesting scoop.it by Luis about the «myth» of super influencers in the cyberspace. How collective intelligence really works !

Scooped by luiy
Scoop.it!

#ANAMIA EGOCENTER : Three #tools to visualize #personal networks | #dataviz #ethnography

#ANAMIA EGOCENTER : Three #tools to visualize #personal networks | #dataviz #ethnography | Influence et contagion | Scoop.it
Data visualization techniques are enjoying ever greater popularity, notably thank to the recent boom of Big Data and our increased capacity to handle large datasets. Network data visualization tech...
luiy's insight:

Three tools to visualize personal networks

Data visualization techniques are enjoying ever greater popularity, notably thank to the recent boom of Big Data and our increased capacity to handle large datasets. Network data visualization techniques are no exception. in fact, appealing diagrams of social connections (sociograms) have been at the heart of the field of social network analysis since the 1930s, and have contributed a lot to its success. Today, all this is evolving at unprecedented pace.

In line with these tendencies, the research team of the project ANAMIA (a study of the networks and online sociability of persons with eating disorders, funded by the French ANR) of which I was one of the investigators, have developed new software tools for the visualization of personal network data, with different solutions for the three stages of data collection, analysis, and dissemination of results.

 

Specifically:

- ANAMIA EGOCENTER is a graphical version of a name generator, to be embedded in a computer-based survey to collect personal network data. It has turned out to be a user-friendly, highly effective interface for interacting and engaging with survey respondents;

more...
No comment yet.
Scooped by luiy
Scoop.it!

Mapping the Spread of Viruses / #Contagions via Contact Tracing | #SNA

Mapping the Spread of Viruses / #Contagions via Contact Tracing | #SNA | Influence et contagion | Scoop.it
The Spread of a Contagion through a Human Network
luiy's insight:

Mapping the Spread of #Contagions via Contact Tracing

A contagion passed by human contact, such as SARS or TB, spreads through human networks based on how infectious and susceptible each party is. Multiple contacts with infectious others play a role in the probability of infection. Contagions that flow through human-based networks can be bad(disease, gossip), good(ideas and information) or neutral(money and investments).

 

Public health officials perform contact tracing to map the spread of the infection and manage its diffusion. The network map above, created at the epidemiology unit of The Centers for Disease Control [CDC], shows the spread of an airborne infectious disease. The map was created using actual contact data from the community in which the outbreak was happening.

 

Black nodes are persons with clinical disease (and are potentially infectious), pink nodes represent exposed persons with incubating (or dormant) infection and are not infectious, green represent exposed persons with no infection and are notinfectious. The infection status is unknown for the grey nodes.

 

Unfortunately the 'social butterfly' in this community, the black node in the center of the graph, is also the most infectious -- a super spreader.

 

Current procedures focus on inoculating the vulnerable -- often the very young and the very old. Network analysis tells us that it may be smarter, and more efficient, to focus on the spreaders -- those with many contacts to many groups.

more...
No comment yet.
Rescooped by luiy from Social Network Analysis #sna
Scoop.it!

The Simple Rules of Social #Contagion | #behaviors

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 significantly more complex than the prediction of the pathogen model. 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 the 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 apply our model to real-time forecasting of user behavior.

 

The Simple Rules of Social Contagion
Nathan O. Hodas, Kristina Lerman

http://arxiv.org/abs/1308.5015


Via Complexity Digest, Shaolin Tan, ukituki
more...
António F Fonseca's curator insight, December 23, 2013 4:12 AM

Another paper about information propagation. A study on the user interface of two social sites, mainly the problem of limited attention and attention managment.

Scooped by luiy
Scoop.it!

The Ripple Effect: Emotional #Contagion and its #Influence on Group Behavior

luiy's insight:

Group emotional contagion, the transfer of moods among people in a group, and its influence on work group dynamics was examined in a laboratory study of managerial decision making using multiple, convergent measures of mood, individual attitudes, behavior, and group-level dynamics. Using a 2 times 2 experimental design, with a trained confederate enacting mood conditions, the predicted effect of emotional contagion was found among group members, using both outside coders' ratings of participants' mood and participants' self-reported mood. No hypothesized differences in contagion effects due to the degree of pleasantness of the mood expressed and the energy level with which it was conveyed were found. There was a significant influence of emotional contagion on individual-level attitudes and group processes. As predicted, the positive emotional contagion group members experienced improved cooperation, decreased conflict, and increased perceived task performance. Theoretical implications and practical ramifications of emotional contagion in groups and organizations are discussed.

more...
No comment yet.
Scooped by luiy
Scoop.it!

What Facebook and Twitter Reveal About #Contagion | #health

What Facebook and Twitter Reveal About #Contagion | #health | Influence et contagion | Scoop.it
All that chatter on social media may be more valuable than we think, say researchers who are mining the postings for clues about how to best control infectious disease.
luiy's insight:

What people share on social media can sometimes predict the spread of ideas about diseases like the flu, for example, or beliefs about vaccinations. The researchers looked at the social media reactions to issues like childhood immunizations, acceptance of quarantine during the SARS outbreak and public health messages related to infections like influenza.

 

“If highly connected nodes in the social network (such as celebrities) suggest that the vaccine carries risks, the resulting perception of vaccine risks can propagate quickly through the social network, fueling a vaccine scare and a drop in vaccine coverage,” they write. Cough cough, Jenny McCarthy. But such social connectivity can also help to prevent biological contagion through imitated or culturally promoted behaviors, like covering your mouth when you cough. And control of the SARS virus was largely possible because the general public was accepting of the quarantines.



Read more: http://healthland.time.com/2013/10/03/what-facebook-and-twitter-reveal-about-contagion/#ixzz2gnWcPCFi

more...
No comment yet.
Scooped by luiy
Scoop.it!

The Dynamics of #Viral Marketing | #datascience #contagion

more...
No comment yet.
Scooped by luiy
Scoop.it!

How Gangnam Style" Went #Viral | #SNA #contagion #datascience

How Gangnam Style" Went #Viral | #SNA #contagion #datascience | Influence et contagion | Scoop.it
Data scientists trace how the most-viewed video in YouTube history spread across the Internet
luiy's insight:

When South Korean pop star Psy released his “Gangnam Style” video in 2012 it spread like wildfire. Researchers at Indiana University Bloomington tracked the spreading meme by following how Twitter users shared the video with friends and strangers alike. By the time 200 tweets had linked to the video among the subset of Twitter users studied, “Gangnam Style” had already reached 86 different communities of users (blue nodes). After 3,000 tweets the meme had spread to nearly 1,000 different communities (green). “Gangnam Style” soon became the most-viewed video in YouTube history; by late 2013, the video had amassed more than 1.8 billion views.

more...
No comment yet.
Scooped by luiy
Scoop.it!

#Virality Prediction and Community Structure in Social Networks | #SNA #memes #contagion

#Virality Prediction and Community Structure in Social Networks | #SNA #memes #contagion | Influence et contagion | Scoop.it
How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily.
luiy's insight:

Our method aims to discover viral memes. To label viral memes, we rank all memes in our dataset based on numbers of tweets or adopters, and define a percentile threshold. A threshold of θT or θUmeans that a meme is deemed viral if it is mentioned in more tweets than θT% of the memes, or adopted by more users than θU% of the memes, respectively. All the features are computed based on the first 50 tweets for each hashtag h. Two baselines are set up for comparison. Random guessselects nviral memes at random, where nviral is the number of viral memes in the actual data.Community-blind prediction employs the same learning algorithm as ours but without the community-based features. We compute both precision and recall for evaluation; the former measures the proportion of predicted viral memes that are actually viral in the real data, and the latter quantifies how many of the viral memes are correctly predicted. Our community-based prediction excels in both precision and recall, indicating that communities are helpful in capturing viral memes (Fig. 5). For example, when detecting the most viral memes by users (θU = 90), our method is about seven times as precise as random guess and over three times as precise as prediction without community features. We achieve a recall over 350% better than random guess and over 200% better than community-blind prediction. Similar results are obtained using different community detection methods or different types of social network links (see SI).

more...
No comment yet.
Scooped by luiy
Scoop.it!

Building A Social Network Of #Crime | #SNA #influence

Building A Social Network Of #Crime | #SNA #influence | Influence et contagion | Scoop.it
Can software distill mayhem into a database?
luiy's insight:

ORCA (Organizational, Relationship, and Contact Analyzer) started by linking people who had been arrested together—the most objective way a record shows that people have, at the very least, been at the same place at the same time. From there, it categorized those who had admitted a gang affiliation. And then, based on social links, it gave the others a numerical probability of a particular affiliation. ORCA further analyzed clustered nodes within the network to identify groups and subgroups—a crew occupying a street corner, for example. By zeroing in on people connected across many groups and subgroups, ORCA singled out the most influential ones.

more...
No comment yet.
Rescooped by luiy from Non-Equilibrium Social Science
Scoop.it!

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
more...
No comment yet.
Scooped by luiy
Scoop.it!

Multiplex networks and interest group #influence reputation: An exponential random graph model I #SNA #datascience

Multiplex networks and interest group #influence reputation: An exponential random graph model I #SNA #datascience | Influence et contagion | Scoop.it
luiy's insight:

This article argues that the embeddedness of interest groups in multiplex networks is an important explanation for variation in interest group influence reputations. Interest groups participate in and learn about the political process through their communication with other groups, collaboration in coalitions, and advocacy in issue areas. As a group engages in communication, collaboration, and issue advocacy, its performance of multiple roles is visible to other interested observers that use this information to make judgments about the group’s contribution (positive or negative) to policy debates. Thus, examining the multiple ways in which interest groups are connected and disconnected helps to account for how their representatives see and think about the community of which they are a part, as well as how they are seen by that community. 

more...
No comment yet.
Scooped by luiy
Scoop.it!

#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.

more...
No comment yet.
Scooped by luiy
Scoop.it!

Selection Effects in Online #Sharing: Consequences for Peer Adoption | #contagion

luiy's insight:

Most models of social contagion take peer exposure to be a corollary of adoption, yet in many settings, the visibility of one's adoption behavior happens through a separate decision process. In online systems, product designers can define how peer exposure mechanisms work: adoption behaviors can be shared in a passive, automatic fashion, or occur through explicit, active sharing. The consequences of these mechanisms are of substantial practical and theoretical interest: passive sharing may increase total peer exposure but active sharing may expose higher quality products to peers who are more likely to adopt. 


We examine selection effects in online sharing through a large-scale field experiment on Facebook that randomizes whether or not adopters share Offers (coupons) in a passive manner. We derive and estimate a joint discrete choice model of adopters' sharing decisions and their peers' adoption decisions. Our results show that active sharing enables a selection effect that exposes peers who are more likely to adopt than the population exposed under passive sharing. 
We decompose the selection effect into two distinct mechanisms: active sharers expose peers to higher quality products, and the peers they share with are more likely to adopt independently of product quality. Simulation results show that the user-level mechanism comprises the bulk of the selection effect. The study's findings are among the first to address downstream peer effects induced by online sharing mechanisms, and can inform design in settings where a surplus of sharing could be viewed as costly.

more...
No comment yet.
Scooped by luiy
Scoop.it!

¿#Guerra civil en Michoacán? | #mexico,, la plaza en lucha por el narco-gobierno, #derechoshumanos #offline

¿#Guerra civil en Michoacán? | #mexico,, la plaza en lucha por el narco-gobierno, #derechoshumanos #offline | Influence et contagion | Scoop.it
Ningún funcionario se atreve a decirlo. No hay gobierno municipal, estatal o federal que se decida a llamar a las cosas por su nombre. Pero lo que sucede en Mi
luiy's insight:

Ningún funcionario se atreve a decirlo. No hay gobierno municipal, estatal o federal que se decida a llamar a las cosas por su nombre. Pero lo que sucede en Michoacán se parece mucho a una guerra civil. Le pueden llamar equis. Pueden pedir suspensión de garantías individuales o disolver los poderes como solicitó el Partido Acción Nacional. Pueden mandar miles de soldados y marinos; de policías federales, estatales y municipales. Pueden militarizar al 100 por ciento el estado, pero lo que sucede en Michoacán se parece a una guerra civil. Y si el gobierno de Enrique Peña Nieto no quiere verlo, si quiere invisibilizarlo, si pretende que miremos a otro lado, comete un grave error. El hecho de ignorar o tapar un problema no lo elimina.

more...
No comment yet.
Scooped by luiy
Scoop.it!

The Dark Corners of the Internet | #SNA #dataviz

The Dark Corners of the Internet | #SNA #dataviz | Influence et contagion | Scoop.it
The way information spreads through society has been the focus of intense study in recent years. This work has thrown up…
luiy's insight:

The way information spreads through society has been the focus of intense study in recent years. This work has thrown up some dramatic results; it explains why some ideas become viral while others do not, why certain individuals are more influential than others and how best to exploit the properties of a network to spread information most effectively.

 

But today, Chuang Liu at Hangzhou Normal University in China and a few pals have a surprise. They say that when information spreads, there are always blind spots in a network that never receive it. And these unreachable dark corners of the network can be numerous and sizeable.

 

Until now theorists have predicted that information can always spread until it saturates a network to the point where everybody has received it. These predictions are come from models based on our understanding of diseases and the way they percolate through a population. The basic assumption is that information spreads in the same way.

more...
Marco Valli's curator insight, January 11, 3:36 AM

A different view on information spread and diffusion on a network. A simple model, accounting for the key difference between "viruses" and "information", both from the sender and the receiver point of view.

Scooped by luiy
Scoop.it!

Distinguishing #influence - based #contagion from #homophily - driven #diffusion in dynamic networks | #dataviz

luiy's insight:

Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300–700%, and that homophily explains >50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.

more...
No comment yet.
Rescooped by luiy from Complex Networks Everywhere
Scoop.it!

#Contagion of Cooperation in Static and Fluid Social Networks

Cooperation is essential for successful human societies. Thus, understanding how cooperative and selfish behaviors spread from person to person is a topic of theoretical and practical importance. Previous laboratory experiments provide clear evidence of social contagion in the domain of cooperation, both in fixed networks and in randomly shuffled networks, but leave open the possibility of asymmetries in the spread of cooperative and selfish behaviors. Additionally, many real human interaction structures are dynamic: we often have control over whom we interact with. Dynamic networks may differ importantly in the goals and strategic considerations they promote, and thus the question of how cooperative and selfish behaviors spread in dynamic networks remains open. Here, we address these questions with data from a social dilemma laboratory experiment. We measure the contagion of both cooperative and selfish behavior over time across three different network structures that vary in the extent to which they afford individuals control over their network ties. We find that in relatively fixed networks, both cooperative and selfish behaviors are contagious. In contrast, in more dynamic networks, selfish behavior is contagious, but cooperative behavior is not: subjects are fairly likely to switch to cooperation regardless of the behavior of their neighbors. We hypothesize that this insensitivity to the behavior of neighbors in dynamic networks is the result of subjects’ desire to attract new cooperative partners: even if many of one’s current neighbors are defectors, it may still make sense to switch to cooperation. We further hypothesize that selfishness remains contagious in dynamic networks because of the well-documented willingness of cooperators to retaliate against selfishness, even when doing so is costly. These results shed light on the contagion of cooperative behavior in fixed and fluid networks, and have implications for influence-based interventions aiming at increasing cooperative behavior.

 

Jordan JJ, Rand DG, Arbesman S, Fowler JH, Christakis NA (2013) Contagion of Cooperation in Static and Fluid Social Networks. PLoS ONE 8(6): e66199. http://dx.doi.org/10.1371/journal.pone.0066199


Via Complexity Digest, Alejandro J. Alvarez S.
more...
wintrotech's curator insight, September 21, 2013 4:40 AM

the new domain hasing and domain selection is always help in good domain rankinh.