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Rescooped by Jean-Michel Livowsky from Influence et contagion
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Strongly Connected Component | #SNA #datascience

Strongly Connected Component | #SNA #datascience | Intelligence | Scoop.it

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luiy's curator insight, September 15, 2014 12:48 PM

Graph connectivity is of special interest in networking, search, shortest path and many other applications.

 

Strongly connected directed graph has a path from all vertices to all vertices.

 

Strongly connected components (SCC) are the strongly connected subgraphs.

 

 - abe, fg, cd and h are the strongly connected subgraphs of G.

 

 

Rescooped by Jean-Michel Livowsky from e-Xploration
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Israel, Gaza, #War & Data | #SNA #socialmedia

Israel, Gaza, #War & Data | #SNA #socialmedia | Intelligence | Scoop.it
social networks and the art of personalizing propaganda

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luiy's curator insight, August 9, 2014 6:11 AM

It’s hard to shake away the utterly depressing feeling that comes with news coverage these days. IDF and Hamas are at it again, a vicious cycle of violence, but this time it feels much more intense. While war rages on the ground in Gaza and across Israeli skies, there’s an all-out information war unraveling in social networked spaces.

 

Not only is there much more media produced, but it is coming at us at a faster pace, from many more sources. As we construct our online profiles based on what we already know, what we’re interested in, and what we’re recommended, social networks are perfectly designed to reinforce our existing beliefs. Personalized spaces, optimized for engagement, prioritize content that is likely to generate more traffic; the more we click, share, like, the higher engagement tracked on the service. Content that makes us uncomfortable, is filtered out.

Rescooped by Jean-Michel Livowsky from e-Xploration
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An Interactive Introduction to Network Analysis and Representation | #SNA #tools

An Interactive Introduction to Network Analysis and Representation | #SNA #tools | Intelligence | Scoop.it

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luiy's curator insight, March 17, 2014 8:54 AM

This interactive application is designed to provide an overview of various network analysis principles used for analysis and representation. It also provides a few examples of untraditional networks used in digital humanities scholarship. Finally, along with the various methods described interactively here are links to related scholarship.

Rescooped by Jean-Michel Livowsky from Influence et contagion
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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 | Intelligence | 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, 2014 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, 2014 8:23 AM

Very intetesting! Attention spans!

Rescooped by Jean-Michel Livowsky from e-Xploration
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#BigData Investment Map 2014 | #dataviz #SNA via @furukama

#BigData Investment Map 2014 | #dataviz #SNA via @furukama | Intelligence | Scoop.it

by BENEDIKT KOEHLER on 1. FEBRUAR 2014


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luiy's curator insight, February 2, 2014 8:42 AM

Here’s an updated version of our Big Data Investment Map. I’ve collected information about ca. 50 of the most important Big Data startups via the Crunchbase API. The funding rounds were used to create a weighted directed network with investments being the edges between the nodes (investors and/or startups). If there were multiple companies or persons participating in a funding round, I split the sum between all investors.

Rescooped by Jean-Michel Livowsky from e-Xploration
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A smart local moving #algorithm for large-scale modularity-based community detection | #SNA #clustering

A smart local moving #algorithm for large-scale modularity-based community detection | #SNA #clustering | Intelligence | Scoop.it

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luiy's curator insight, November 15, 2013 10:41 AM

Our smart local moving (SLM) algorithm is an algorithm for community detection (or clustering) in large networks. The SLM algorithm maximizes a so-called modularity function. The algorithm has been successfully applied to networks with tens of millions of nodes and hundreds of millions of edges. The details of the algorithm are documented in a paper (preprint available here).

 

The SLM algorithm has been implemented in the Modularity Optimizer, a simple command-line computer program written in Java. The Modularity Optimizer can be freely downloaded. The program can be run on any system that supports Java version 1.6 or higher. In addition to the SLM algorithm, the Modularity Optimizer also provides an implementation of the well-known Louvain algorithm for large-scale community detection developed by Vincent Blondel and colleagues. An extension of the Louvain algorithm with a multilevel refinement procedure, as proposed by Randolf Rotta and Andreas Noack, is implemented as well. All algorithms implemented in the Modularity Optimizer support the use of a resolution parameter to determine the granularity level at which communities are detected.

Jean-Michel Livowsky's curator insight, November 16, 2013 8:38 AM

SLM algoritm. Very nice move in this complex approach of collective intelligence.

Rescooped by Jean-Michel Livowsky from e-Xploration
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A smart local moving #algorithm for large-scale modularity-based community detection | #SNA #clustering

A smart local moving #algorithm for large-scale modularity-based community detection | #SNA #clustering | Intelligence | Scoop.it

Via luiy
Jean-Michel Livowsky's insight:

SLM algoritm. Very nice move in this complex approach of collective intelligence.

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luiy's curator insight, November 15, 2013 10:41 AM

Our smart local moving (SLM) algorithm is an algorithm for community detection (or clustering) in large networks. The SLM algorithm maximizes a so-called modularity function. The algorithm has been successfully applied to networks with tens of millions of nodes and hundreds of millions of edges. The details of the algorithm are documented in a paper (preprint available here).

 

The SLM algorithm has been implemented in the Modularity Optimizer, a simple command-line computer program written in Java. The Modularity Optimizer can be freely downloaded. The program can be run on any system that supports Java version 1.6 or higher. In addition to the SLM algorithm, the Modularity Optimizer also provides an implementation of the well-known Louvain algorithm for large-scale community detection developed by Vincent Blondel and colleagues. An extension of the Louvain algorithm with a multilevel refinement procedure, as proposed by Randolf Rotta and Andreas Noack, is implemented as well. All algorithms implemented in the Modularity Optimizer support the use of a resolution parameter to determine the granularity level at which communities are detected.

Rescooped by Jean-Michel Livowsky from Influence et contagion
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A taxonomy of #clustering procedures | #datascience


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Rescooped by Jean-Michel Livowsky from Influence et contagion
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The evolution of #memes on Facebook | #SNA #contagion

The evolution of #memes on Facebook | #SNA #contagion | Intelligence | Scoop.it

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luiy's curator insight, June 22, 2014 10:36 AM

A meme is an idea that is readily transmitted from person to person. But we humans are not perfect transmitters. While sometimes we repeat the idea exactly, often we change the meme, either unintentionally, or to embellish or improve it. 

 

Take for example, the meme: 

“No one should die because they cannot afford health care, and no one should go broke because they get sick. If you agree, post this as your status for the rest of the day”. 

 

In September of 2009, over 470,000 Facebook users posted this exact statement as their status update. At some point someone created a variant by prepending "thinks that'' (which would follow the individual's name, e.g., “Sam thinks that no one…”), which was copied 60,000 times. The third most popular variant inserted "We are only as strong as the weakest among us'' in the middle. “The rest of the day” at one point (probably in the late evening hours) became “the next 24 hours”. Others abbreviated it to “24 hrs”, or extended it to “the rest of the week”.

 

 

Modeling memes as genes

 

So can memes really be modeled as genes? After all, Richard Dawkins originally coined the word "meme” to draw the analogy to genes when describing how ideas or messages replicate and evolve[1]. How would one test the hypothesis that memes undergo a process akin to biological evolution? First, tracing biological evolution is notoriously difficult because one must discern the lineage of specific genetic sequences through generations, without having the genetic sequence of many intermediate instances. But when studying Facebook memes, we have a very unique opportunity* to actually trace when copies and mutations occurred, and these are the two basic ingredients in the evolutionary process.

Rescooped by Jean-Michel Livowsky from Influence et contagion
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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 | Intelligence | 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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Rescooped by Jean-Michel Livowsky 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 | Intelligence | 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, 2014 8:27 PM
Bienvenu Luis
Rescooped by Jean-Michel Livowsky from Influence et contagion
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Modeling #Emotion #Influence from Images in Social Networks | #SNA

Modeling #Emotion #Influence from Images in Social Networks | #SNA | Intelligence | Scoop.it

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luiy's curator insight, January 21, 2014 9:37 AM

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.

Rescooped by Jean-Michel Livowsky from e-Xploration
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A smart local moving #algorithm for large-scale modularity-based community detection | #SNA #clustering

A smart local moving #algorithm for large-scale modularity-based community detection | #SNA #clustering | Intelligence | Scoop.it

Via luiy
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luiy's curator insight, November 15, 2013 10:41 AM

Our smart local moving (SLM) algorithm is an algorithm for community detection (or clustering) in large networks. The SLM algorithm maximizes a so-called modularity function. The algorithm has been successfully applied to networks with tens of millions of nodes and hundreds of millions of edges. The details of the algorithm are documented in a paper (preprint available here).

 

The SLM algorithm has been implemented in the Modularity Optimizer, a simple command-line computer program written in Java. The Modularity Optimizer can be freely downloaded. The program can be run on any system that supports Java version 1.6 or higher. In addition to the SLM algorithm, the Modularity Optimizer also provides an implementation of the well-known Louvain algorithm for large-scale community detection developed by Vincent Blondel and colleagues. An extension of the Louvain algorithm with a multilevel refinement procedure, as proposed by Randolf Rotta and Andreas Noack, is implemented as well. All algorithms implemented in the Modularity Optimizer support the use of a resolution parameter to determine the granularity level at which communities are detected.

Jean-Michel Livowsky's curator insight, November 16, 2013 8:38 AM

SLM algoritm. Very nice move in this complex approach of collective intelligence.

Rescooped by Jean-Michel Livowsky from e-Xploration
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Oycib ::: Collective Intelligence. "Kaan". Network Visualisation.

Oycib ::: Collective Intelligence. "Kaan". Network Visualisation. | Intelligence | Scoop.it

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luiy's curator insight, May 2, 2013 8:52 AM

Beginning with the origins, Oycib means in Mayan language "the place of honey". In this projet, Oycib is an e-Research infrastructure for the Collective Intelligence Analysis.

 

With Oycib infrastructure we propose an analysis model, based in the digital practices and collaboration profiles for the development of Social Learning and the Context Awareness in the Collective Intelligence process.

 

The infrastructure design and the profiles proposed here, are based on historical studies about social organization glyphs in Mayan culture made by Montgomery (2002) and Calvin (2012).

 

Initially we worked with four collaboration profiles: the "Itzaat", the "Pitziil", the "Ayuxul" and the "Sajal" (profiles), but we can find others depending of the organization context. Thus, it's important to mention that each profile is found based on the e-Xploración model and they are the qualitative and quantitative interpretation of the collaborative practices. In this way, we propose methods based on Social Network Analysis for the learning and knowledge management.

 

Thus, the network in Oycib is called "Kaan" (sky or network in Mayan Lenguage). In the "Kaan" we present the visualization of the subjects and objects, such as persons, forums, blogs, files, groups and all the interactions among them. Additionally, each profile and their interactions is presented.

 

... you can interact with "Kaan" here.

 

http://viz.oycib.org/net_all_3/network/index.html