Nodes and edges
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Nodes and edges
What happens when you start seeing the world as a set of networks? You gain a new perspective and valuable insights.
Curated by Lamia Ben
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A Network Way of Working: A Compilation of Considerations about Effectiveness in Networks

A Network Way of Working: A Compilation of Considerations about Effectiveness in Networks | Nodes and edges | Scoop.it
Networks aren’t new, but the role they play in our working lives is expanding significantly through technology. The potential for impact is great, but newly enhanced networks require new strategies.

Via june holley
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Ivon Prefontaine's curator insight, July 18, 6:33 PM

This is an excellent article with a detailed bibliography. It would be worthwhile for School managers to read and begin to realize networks are more organic and complex than just externally ordering them and insisting they happen.

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Understanding Email Traffic: Social Network Analysis Meets Language Modeling | blog | graus.nu

Understanding Email Traffic: Social Network Analysis Meets Language Modeling | blog | graus.nu | Nodes and edges | Scoop.it
Understanding Email Traffic: Social Network Analysis Meets Language Modeling: http://t.co/WZg65VStWk #SNA #SIGIR2014
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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Nodes and edges | Scoop.it

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luiy's curator insight, March 27, 10:44 AM

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

António F Fonseca's curator insight, April 2, 3:01 AM

Another paper about popularity prediction.

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Network Science Book - you can download the book here | #SNA

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

Via Claude Emond, luiy, Jens Martinus
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Claude Emond's comment, February 8, 5:27 PM
Bienvenu Luis
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You Know Who Else Collected Metadata? The Stasi.

You Know Who Else Collected Metadata? The Stasi. | Nodes and edges | Scoop.it
Files obtained from the archives of the East German secret police show how far technology of spycraft has come.
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Social Network Analysis: Full Network Maps : Pacific RISA – Managing Climate Risk in the Pacific

Social Network Analysis: Full Network Maps : Pacific RISA – Managing Climate Risk in the Pacific | Nodes and edges | Scoop.it

"The images on this page can be used to broadly understand the general structure of our professional network. For example, we see strong country clusters but we also see many strong connections between clusters. This will not be surprising to anyone in the Pacific—oftentimes our first conversation with a stranger is ‘who do we both know?’ to establish mutual friends and colleagues. In the first network map below, you can see broad trends of connection and centrality within the network. If you download the high resolution version of the second map below, you will be able to see individuals’ names and their connections to others. Exploring these maps, you can begin to look for connections that your friends and colleagues have. When considering future collaborations, you can explore who knows who within the region." ...


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Social Network Analysis: New in Mathematica 9

Social Network Analysis: New in Mathematica 9 | Nodes and edges | Scoop.it
Complete set of state-of-the art social network analysis functions. High level functions for community detection, cohesive groups, centrality, and similarity measures.
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Divided Edge Bundling for Directional Network Data I #SNA #dataviz

Divided Edge Bundling for Directional Network Data I #SNA #dataviz | Nodes and edges | Scoop.it

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luiy's curator insight, December 20, 2013 4:55 AM

The node-link diagram is an intuitive and venerable way to depict a graph. To reduce clutter and improve the readability of node-link views, Holten & van Wijk's force-directed edge bundling employs a physical simulation to spatially group graph edges. While both useful and aesthetic, this technique has shortcomings: it bundles spatially proximal edges regardless of direction, weight, or graph connectivity. As a result, high-level directional edge patterns are obscured. We present divided edge bundling to tackle these shortcomings. By modifying the forces in the physical simulation, directional lanes appear as an emergent property of edge direction. By considering graph topology, we only bundle edges related by graph structure. Finally, we aggregate edge weights in bundles to enable more accurate visualization of total bundle weights. We compare visualizations created using our technique to standard force-directed edge bundling, matrix diagrams, and clustered graphs; we find that divided edge bundling leads to visualizations that are easier to interpret and reveal both familiar and previously obscured patterns.

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SNA TIG Week: Todd Honeycutt on Missing Data in Social Network Analysis

SNA TIG Week: Todd Honeycutt on Missing Data in Social Network Analysis | Nodes and edges | Scoop.it

When we have missing data, we are making inferences from a partial network. Such results can be misleading, particularly if the data are not missing randomly.

 

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Doing Social Network Analysis using Qualitative Methods

Doing Social Network Analysis using Qualitative Methods | Nodes and edges | Scoop.it

"This interesting lecture by Nick Crossley and Gemma Edwards offers a helpful introduction to social network analysis before discussing purely qualitative approaches to analysing social networks. "

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Doris Schuster's curator insight, December 2, 2013 12:38 PM

good introduction to doing network analysis with qualitative methods!

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Connected Insight Summit 2013

Connected Insight Summit 2013 | Nodes and edges | Scoop.it
Solve the toughest problems in your industry. Connect with the leaders who are using the power of social networks to win. 
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The 3rd annual Connected Insight Summit has quite an interesting Agenda!

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Algorithm Seeks Meaningful Relationships

Algorithm Seeks Meaningful Relationships | Nodes and edges | Scoop.it

In the August issue of Nature Biotechnology, a team led by Broad associate member Manolis Kellis introduced a new algorithm that may make this work easier for network researchers in a variety of fields. The team developed a method called “network deconvolution” that helps researchers identify the most direct connections within complex networks by filtering out the background noise caused by indirect or incidental connections.


Via Kenneth Mikkelsen
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Leveraging social networks for improved performance

My network presentation updated with some new slides.
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Jay Ratcliff's curator insight, September 4, 2013 7:32 AM

Nice presentation about SNA and ONA.

 

I liked that it covered some aspects of data collection as well as analysis.

Being new to the subject that helps me focus on how to get the information about the networks I want to monitor.

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An Interactive Introduction to Network Analysis and Representation | #SNA #tools

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

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luiy's curator insight, March 17, 5:54 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.

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MIT Lincoln Laboratory: Publications: Current Journal

MIT Lincoln Laboratory: Publications: Current Journal | Nodes and edges | Scoop.it

The Lincoln Laboratory Journal showcases some of the Laboratory's most innovative and high-impact work, in fields ranging from air traffic control to bioagent sensing to parallel computing. The Journal consists of in-depth feature articles written by Laboratory staff members as well as shorter "Lab Notes" written by the Journal editors.

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MIT's Lincoln Laboratory Journal's special issue focuses on graphs and networks.

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Using SNA to support networked learning environments

Using SNA to support networked learning environments | Nodes and edges | Scoop.it

SNAPP: Realising the affordances of real-time SNA within networked learning environments

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A presentation on the insights offered by Social Network Analysis to diagnose students' behavioral patterns on the Moodle platform

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Origin of Peer Influence in Social Networks

Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends’ friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be.
DOI: http://dx.doi.org/10.1103/PhysRevLett.112.098702

Origin of Peer Influence in Social Networks
Phys. Rev. Lett. 112, 098702 – Published 6 March 2014
Flávio L. Pinheiro, Marta D. Santos, Francisco C. Santos, and Jorge M. Pacheco


Via Complexity Digest
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Eli Levine's curator insight, March 10, 2:16 PM

Indeed, we are all interconnected in very profound and subtle ways, whether we accept it or not.


This one's for the Libertarians and conservatives out there, who don't seem to think that their actions effect the other, or that the other can effect them, or that the actions done onto the other will effect the actions that are done onto them by the other.

 

Kind of like how they blame the poor for being angry at the rich, after the poor produced the wealth that engorges the rich.

 

Silly people....

 

Think about it.

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The Potential of Social Network Analysis in Intelligence

The Potential of Social Network Analysis in Intelligence | Nodes and edges | Scoop.it
Within its limits, SNA can be applied to identify individuals or organizations within a network, generate new leads and simulate flows of information or money throughout a network.

Via ukituki
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ukituki's curator insight, January 10, 1:33 AM

Like every analytic technique, SNA has great utility for the right question. Within its limits, SNA is unmatched and can be usefully applied to identify key individuals or organizations within a network, generate new leads and simulate the flows of information or money throughout a network.  SNA, however, remains just an answer, not the answer.  Used inappropriately or without a full understanding of the limits of the method and analysts will only be finding new and more technically sophisticated ways to fail.  That, then, is the primary job of the modern day analyst: making the judgment call of which techniques to use and when.  Equally as important as knowing when to use SNA is knowing when not to use it.

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Social data interpretation: The human factor

Social data interpretation: The human factor | Nodes and edges | Scoop.it
Research claim that A full 90% of all the data in the world has been generated over the last two years. This tsunami of digital data have brought along incredible insights but also many many headac...
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Inventing the Social Network | Boston Review

Inventing the Social Network | Boston Review | Nodes and edges | Scoop.it
Origins of social network analysis b4 FB & Zuck http://t.co/BGxcC04rfL
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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 | Nodes and edges | 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
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luiy's curator insight, December 19, 2013 4:49 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.

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PLOS ONE: Network Centrality of Metro Systems

PLOS ONE: Network Centrality of Metro Systems | Nodes and edges | 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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Study: More than race or poverty, social networks predict victims of fatal shootings in Chicago - Chicago Sun-Times

Study: More than race or poverty, social networks predict victims of fatal shootings in Chicago - Chicago Sun-Times | Nodes and edges | Scoop.it
Race and poverty are not as important as a person’s social network in predicting whether he or she will become a victim of a fatal shooting in Chicago, Yale University sociologists found in a study released Thursday.
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If A Network Is Broken, Break It More

If A Network Is Broken, Break It More | Nodes and edges | 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...
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New research suggests that by selectively damaging part of a broken network, we can bring the entire system to a better state.

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Social network analysis and complexity book list

@Soc_Net_Intel This is a useful link: http://t.co/GS05hliqAL #recommendedreading #SNA Keep up the good work!
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An extensive list of SNA related books.

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ukituki's curator insight, September 1, 2013 5:00 AM

Topics include:

General Audience ReadingsBasics and MethodologyFor Consultants, Managers and LeadersI use these for teaching Web Science I+II at RWTH Aachen UniversityAgents, Dynamics, Games and Adapation 
Jay Ratcliff's curator insight, September 4, 2013 6:47 AM

Very useful list.