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Rescooped by Jens Martinus from Start-Up & Growth Hacking Tips
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Be Data Informed Without Being a Data Scientist

You are a designer, or a coder, or a manager. Maybe you are even a unicorn. But you are not a data scientist. Still, you want to get more out of the mountain o…

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Rescooped by Jens Martinus from Influence et contagion
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Network Science Book - you can download the book here | #SNA

Network Science Book - you can download the book here | #SNA | ntwrk | Scoop.it
The power of network science, the beauty of network visualization.

Via Claude Emond, luiy
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Claude Emond's comment, February 8, 8:27 PM
Bienvenu Luis
Rescooped by Jens Martinus from Influence et contagion
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What Fuels the Most Influential Tweets? | #influence #SNA #datascience | cc/ @jbmacluckie @jorgaf

What Fuels the Most Influential Tweets? | #influence #SNA #datascience | cc/ @jbmacluckie @jorgaf | ntwrk | Scoop.it
The number of followers you have and the exact wording matter less than you think. What makes a difference is having the right message for the right people.

Via luiy
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luiy's curator insight, February 22, 7:58 AM

"Influence" doesn't necessarily mean what you think it does. In the age of the social-media celebrity, a glut of Twitter followers or particularly pugnacious sampling of pithy updates are often the hallmarks of an influencer. But new research suggests that influence is situational at best: as people compete for the attention of the broader online ecosystem, the relevance of your message to the existing conversation of those around you trumps any innate "power" a person may have.

 

.... According to co-author Vespignani, having millions of followers does not denote an important message. Rather, the messages with the most immediate relevance tend to have a higher probability of resonating within a certain network than others. Think of it as "survival of the fittest" for information: those tweets that capture the most attention, whether related to a major geopolitical or news event or a particular interest, are likely to persist longer. This competition sounds bad, but it's generally good for messages in general: thousands of tweets about Japan's 2011 earthquake or the ongoing conflict in Syria don't cancel each other out, but help refocus the attention of the wider Twitter audience on those issues, which in turn provides an added lift to individual messages over other off-topic ones.

Rescooped by Jens Martinus from Influence et contagion
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The Structure of Online Diffusion Networks I #adoptions #patterns


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luiy's curator insight, November 26, 2013 11:23 AM

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. 

Rescooped by Jens Martinus from Nodes and edges
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“Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis

“Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis | ntwrk | 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, Lamia Ben
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luiy's curator insight, December 19, 2013 7:49 AM

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.

Rescooped by Jens Martinus from Influence et contagion
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Duncan Watts - Social contagion: What do we really know?

Duncan Watts - Social contagion: What do we really know? | ntwrk | Scoop.it

Social contagion: What do we really know? by Duncan Watts


Via Complexity Digest, luiy
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luiy's curator insight, February 9, 2013 10:12 AM

The phenomenon of social contagion—that information, ideas, and even behaviors can spread through networks of people the way that infectious diseases do—is both intuitively appealing and potentially powerful.

It appeals to our intuition for two reasons. First, it is obviously true that people are influenced by one another. Reflecting on our individual experience of life, it is easy to recall any number of instances in which we have been influenced, whether by our parents, our teachers, our coworkers, or our friends, and corresponding instances when we have influenced them. And second, once you accept that one person can influence another, it follows logically that that person can influence yet another person, who can in turn influence another person, and so on. Influence, that is, can spread.

Rescooped by Jens Martinus from Nodes and edges
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If A Network Is Broken, Break It More

If A Network Is Broken, Break It More | ntwrk | Scoop.it
If A Network Is Broken, Break It More by Sophie Bushwick, Inside Science From the World Wide Web to the electrical grid, networks are notoriously difficult to control. A disturbance to just one part...

Via Lamia Ben
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Lamia Ben's curator insight, September 13, 2013 1:03 PM

New research suggests that by selectively damaging part of a broken network, we can bring the entire system to a better state.

Rescooped by Jens Martinus from e-Xploration
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Anatomy of a social network Network | #SNA #dataviz

Anatomy of a social network Network | #SNA #dataviz | ntwrk | Scoop.it
Anatomy of a social network

Network researcher Ron Burt has identified two types of activities that create value in small-world networks: brokerage and…

Via luiy
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luiy's curator insight, July 26, 2013 12:17 PM

Brokerage is about developing the weak ties: building bridges and relationships between clusters. Brokers are in a position to see the differences between groups, to cross-pollinate ideas, and to develop the differences into new ideas and opportunities.

Closure is about developing the strong ties: building alignment, trust, reputation and community within the clusters. Trust-builders are in a position to understand the deep connections that bond the people together and give them common identity and purpose.

These two kinds of activity, bridging and trust-building, demonstrate two very different ways that people and organizations can bring value to a network: Bridging leads to innovation and trust-building leads to group performance. The value that comes from these activities is known as social capital. Like every other form of capital, social capital represents stored value—in this case, relationship value—that can be translated into meaningful and tangible benefits.
The power of an individual node in any network can be considered along three dimensions: Degree, closeness and betweenness. 

Degree is the number of connections a node has to other nodes; for example the number of people in your family, or on your team at work, or the number of “friends” attached to your Facebook account. For an organization it could be the number of sales affiliates or business partners.

The value of a high degree is potential: the potential to connect and interact with a great number of other nodes in the network.

Closeness is a measure of how easily a node can connect with other nodes. For example you are probably very close to your team at work because it’s easy to connect to them: you can contact any person at any time. But you might be further away from other people in your company. Some you might be able to catch by walking down the hall or popping into their office, while to see others you might need an appointment, or you might need to be introduced by a mutual acquaintance. Anyone who has tried to make a connection on LinkedIn knows that the greater the distance, the harder it is to make a connection.

The value of closeness is ease of connection: The shorter the distance between you and other nodes, the fewer network “hops” you need to make, the easier it is for you to make connections when you need to.......

Rescooped by Jens Martinus from Collaboration
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Social Network Analysis and an Introduction to Tools

This presentation covers the basics of network analysis and then goes into the different types of tool that support analyzing networks.


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Conexus

Conexus | ntwrk | Scoop.it
Jens Martinus's insight:

Get a deeper understanding of network effects on LinkedIn by adding networks. 

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In Search of a Network Theory of Innovations by Loet Leydesdorff, Petra Ahrweiler :: SSRN

In Search of a Network Theory of Innovations by Loet Leydesdorff, Petra Ahrweiler :: SSRN | ntwrk | Scoop.it
As a complement to Nelson & Winter’s (1977) article entitled “In Search of a Useful Theory of Innovation,” we argue that a sociological perspective on innovatio
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#SNA : Drug Marketers Use Social Network Diagrams to Help Locate Influential Doctors #health #bigdata

#SNA : Drug Marketers Use Social Network Diagrams to Help Locate Influential Doctors #health #bigdata | ntwrk | Scoop.it
Consulting companies like Activate Networks use social network diagrams to help pharmaceutical marketers identify prescribing histories and relationships among doctors.

Via luiy
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luiy's curator insight, May 20, 2013 10:08 AM

The information allows drug makers to know which drugs a doctor is prescribing and how that compares to a colleague across town. They know whether patients are filling their prescriptions — and refilling them on time. They know details of patients’ medical conditions and lab tests, and sometimes even their age, income and ethnic backgrounds.

The result, said one marketing consultant, is what would happen if Arthur Miller’s Willy Loman met up with the data whizzes of Michael Lewis’s “Moneyball.” “There’s a group of geeks, if you will, who are running the numbers and helping the sales guys be much more efficient,” said Chris Wright, managing director of ZS Associates, which conducts such analyses for pharmaceutical companies.

 

Drug makers say they are putting the information to good use, by helping a doctor improve the chances that their patients take their medications as prescribed, or making sure they are prescribing the right drug to the right patients.

 

Some doctors, however, expressed discomfort with the idea of sensitive data being used to sell drugs, even though federal law requires that any personally identifiable information be removed. “I think the doctors tend not to be aware of the depths to which they are being analyzed and studied by people trying to sell them drugs and other medical products,” said Dr. Jerry Avorn, a professor of medicine at Harvard Medical School and a pioneer of programs for doctors aimed at counteracting the marketing efforts of drug makers. “Almost by definition, a lot of this stuff happens under the radar — there may be a sales pitch, but the doctor may not know that sales pitch is being informed by their own prescribing patterns.”

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How To Detect #Communities Using Social Network Analysis | #SNA

How To Detect #Communities Using Social Network Analysis | #SNA | ntwrk | Scoop.it

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luiy's curator insight, September 16, 10:45 AM

Think of communities as very similar to the segments identified in a brand’s customer segmentation model. (With demographics analysis layered on, you might even find that they’re the same.)

While direct marketing communications is often customized by segment, historically this hasn’t been something brands have done in social. But, using social network analysis and also Twitter & Facebook ad targeting, it’s possible to send specific messages to specific groups of people.

 

Powered by Pulsar TRAC these could be people engaging in a specific conversation, individuals sharing a piece of content online, or the followers of an account on Twitter. Any group of people, in essence, as long as we can define that audience through some property of its behaviour in social media – such as keyword, user bio, or location.

 

Community analysis allows brands to really understand the behavior of their audiences in a way they can’t replicate with offline, non-social data.

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Competition among #memes in a world with limited attention | #SNA #ABM #prediction

Competition among #memes in a world with limited attention | #SNA #ABM #prediction | ntwrk | Scoop.it
The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

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luiy's curator insight, February 22, 8:06 AM

Here we outline a number of empirical findings that motivate both our question and the main assumptions behind our model. We then describe the proposed agent-based toy model of meme diffusion and compare its predictions with the empirical data. Finally we show that the social network structure and our finite attention are both key ingredients of the diffusion model, as their removal leads to results inconsistent with the empirical data.

 

-----------------------------

Limited attention


We first explore the competition among memes. In particular, we test the hypothesis that the attention of a user is somewhat independent from the overall diversity of information discussed in a given period. Let us quantify the breadth of attention of a user through Shannon entropy S = −Σi f(i) log f(i) where f(i) is the proportion of tweets generated by the user about meme i. Given a user who has posted n messages, her entropy can be as small as 0, if all of her posts are about the same meme; or as large as log n if she has posted a message about each of n different memes. We can measure the diversity of the information available in the system analogously, defining f(i) as the proportion of tweets about meme i across all users. Note that these entropy-based measures are subject to the limits of our operational definition of a meme; finer or coarser definitions would yield different values.

 

John Caswell's curator insight, March 2, 8:23 AM

Very intetesting! Attention spans!

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Collective Intelligence in #Design I #CI


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Amaury Van Espen's curator insight, January 21, 9:25 AM

Une approche intéressante et enrichissante de l'Intelligence Collective (en Anglais) qui tranche avec le bricolage ambiant et les pratiques autocratiques des supposés sachant.

COMMON GOOD FORUM's curator insight, January 23, 7:32 AM

Voir en particulier, page 26, sur le langage (comme "bien commun intermédiaire")

See in particular p. 26: on the language (as an intermdediary common good) 

COMMON GOOD FORUM's curator insight, January 23, 7:44 AM

see in particular page 26 on the role and place of the Language (intermediary common good)

 

Voir en particulier page 26 sur le langage, bien commun intermédiaire (comme l'Art, le droit, les médias, les langues..)

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PLOS ONE: Network Centrality of Metro Systems

PLOS ONE: Network Centrality of Metro Systems | ntwrk | Scoop.it

Whilst being hailed as the remedy to the world’s ills, cities will need to adapt in the 21st century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality.


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BayesiaLab 5.0: Analytics, Data Mining, Modeling & Simulation

BayesiaLab 5.0: Analytics, Data Mining, Modeling & Simulation | ntwrk | Scoop.it
The leading software for knowledge management and analytics with Bayesian networks.

Via Pierre Levy
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luiy's curator insight, October 5, 2013 10:43 AM

BayesiaLab is a powerful desktop application (Windows/Mac/Unix) for knowledge discovery, data mining, analytics, predictive modeling and simulation - all based on the paradigm of Bayesian networks. Bayesian networks have become a very powerful tool for deep understanding of very complex, high-dimensional problem domains, ranging from bioinformatics to marketing science.

BayesiaLab is the world’s only comprehensive software package for learning, editing and analyzing Bayesian networks. It provides perhaps the easiest way to practically apply artificial intelligence tools, thus transforming and, more importantly, massively accelerating research workflows.

Fàtima Galan's curator insight, October 7, 2013 6:53 AM

"BayesiaLab is a powerful desktop application (Windows/Mac/Unix) for knowledge discovery, data mining, analytics, predictive modeling and simulation - all based on the paradigm of Bayesian networks. Bayesian networks have become a very powerful tool for deep understanding of very complex, high-dimensional problem domains, ranging from bioinformatics to marketing science."

Rescooped by Jens Martinus from Influence et contagion
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What Kind of Online Influencer Are You? The Klout Influence Matrix

What Kind of Online Influencer Are You? The Klout Influence Matrix | ntwrk | Scoop.it

Robin Good: According to Klout there are at least 16 different types of online influencers, ranging from those who love to actively share and participate, to those who more quitely like to explore, observe and report.  


The Klout Influence Matrix identifies these specific 16 types:

CuratorBroadcasterSyndicator FeederTastemakerCelebrityThought LeaderPunditDabblerConversationalistObserverExplorerSocializerNetworkerActivistSpecialist


Source: Klout.comFull image matrix: http://www.jkspeaks.com/wordpress/wp-content/uploads/2011/10/klout-influence-matrix2.jpg ;

  As in my case, you may likely feel that you belong in more than one of these categories.
 What matters is your ability to develop greater sensitivity for the differences that make up these profiles and to cull more attentively those that you feel are closer to your character and objectives.
 Check also Lisa Barone, co-founder of the firm Outspoken Media (New York), who in contrast, proposes a simpler list in Small Business Trends: The Five Types of Influencers On The Web.

Interesting. 8/10
(Thanks to Raymond Morin) 
Via Robin Good, luiy
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Giua Giovanni Lacqua's comment, February 20, 2012 5:00 AM
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Rescooped by Jens Martinus from All About LinkedIn
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3 Strategies That Helped LinkedIn Dominate Professional Networking

3 Strategies That Helped LinkedIn Dominate Professional Networking | ntwrk | Scoop.it
We spoke with Allen Blue, LinkedIn's co-founder, to learn about the company's early product development, and specifically its thoughts on scaling.

Via Anita Windisman
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Visualized: The world of verified users | Twitter Blog | #dataviz #clusters

Visualized: The world of verified users | Twitter Blog | #dataviz #clusters | ntwrk | Scoop.it
How do some of the world’s most famous people follow each other on Twitter?...

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luiy's curator insight, July 22, 2013 6:35 AM

This beautiful visualization, created during Twitter Hack Week by our very own Isaac Hepworth (@isaach), shows the mutual follows between over 50,000 verified Twitter users — that is, which verified users follow each other.

 

The users in this map are colored by category: blue for news, purple for government and politics, red for music, yellow for sports and green for TV — the five largest categories on Twitter today.

 

One of the many fascinating things about this diagram is that it shows which accounts tend to follow those outside their category. For example, the reason that blue and purple almost seem to merge into one another is that journalists tend to follow politicians, and vice versa. The same is true of TV and music, down in the bottom right, with musicians and TV stars following each other often.

Jay Ratcliff's comment, July 23, 2013 1:54 PM
While I find the graph interesting, I don't like not being able to interact with it in a more dynamic method.
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The arteries of the world, in Tweets

The arteries of the world, in Tweets | ntwrk | Scoop.it
What happens when you plot billions of geotagged Tweets on a map? You can see the arteries of the world.
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Rescooped by Jens Martinus from All About LinkedIn
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A More Personalized LinkedIn Homepage

A More Personalized LinkedIn Homepage | ntwrk | Scoop.it
When we introduced the new look and feel for the LinkedIn Homepage a year ago, our goal was to create a customized experience that would make it easier for you to begin each day armed with the knowledge and insights you need to be productive and...

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Anita Windisman's curator insight, June 27, 2013 3:38 PM

Do you share status updates on LinkedIn? Ever wonder who's engaging?  Now you know!

Rescooped by Jens Martinus from Global Brain
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Visualization of the #GlobalBrain "community" on Twitter| Experiment @Bluenod

Visualization of  the #GlobalBrain "community" on Twitter| Experiment @Bluenod | ntwrk | Scoop.it
Bluenod is a simple way to search and explore communities.

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