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Social Network Analysis #sna
Social Network Analysis
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Workshop in IT: Collaborative Innovation Networks

Workshop in IT: Collaborative Innovation Networks | Social Network Analysis #sna | Scoop.it
Diversity begets creativity—in this seminar we tap the amazing power of swarm creativity on the Web by studying and working together as Collaborative Innovation Networks (COINs).
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All lectures are available in PDFs.

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NodeXL Graph Gallery

NodeXL Graph Gallery | Social Network Analysis #sna | Scoop.it
NodeXL Graph Gallery, a collection of network graphs created by NodeXL.
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Nicholas Christakis - On Health and Social Networks

Nicholas Christakis talks to us about health and social networks. Nicholas is an American physician and social scientist known for his research on social networks…
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Weak telescope is better than no telescope

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The Mesh of Civilizations and International Email Flows

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In The Clash of Civilizations, Samuel Huntington argued that the primary axis of global conflict was no longer ideological or economic but cultural and religious, and that this division would characterize the "battle lines of the future.

 

Our analysis shows that email flows are consistent with Huntington's thesis. In addition to location in Huntington's "civilizations," our results also attest to the importance of both cultural and economic factors in the patterning of inter-country communication ties.

  
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An Autopsy of a Dead Social Network | MIT Technology Review

An Autopsy of a Dead Social Network | MIT Technology Review | Social Network Analysis #sna | Scoop.it
Following the collapse of the social network Friendster, computer scientists have carried out a digital autopsy to find out what went wrong.
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2013 Webinar: Network Analysis (SNA/ONA) Methods for Assessment & Measurement | Leadership Learning Community

2013 Webinar: Network Analysis (SNA/ONA) Methods for Assessment & Measurement | Leadership Learning Community | Social Network Analysis #sna | Scoop.it
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#LAK13: Recipes in capturing and analyzing data -- Twitter

Related post at http://mashe.hawksey.info/2013/02/lak13-recipes-in-capturing-and-analyzing-data-twitter/
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How women organize social networks different from men : Scientific Reports : Nature Publishing Group

How women organize social networks different from men : Scientific Reports : Nature Publishing Group | Social Network Analysis #sna | Scoop.it
Superpositions of social networks, such as communication, friendship, or trade networks, are called multiplex networks, forming the structural backbone of human societies. Novel datasets now allow quantification and exploration of multiplex networks.
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On the individual level females perform better economically and are less risk-taking than males. Males reciprocate friendship requests from females faster than vice versa and hesitate to reciprocate hostile actions of females.


On the network level females have more communication partners, who are less connected than partners of males. We find a strong homophily effect for females and higher clustering coefficients of females in trade and attack networks. Cooperative links between males are under-represented, reflecting competition for resources among males.


These results confirm quantitatively that females and males manage their social networks in substantially different ways.

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The Network Thinkers: Arrows on Twitter

The Network Thinkers: Arrows on Twitter | Social Network Analysis #sna | Scoop.it
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Twitter is a social network that network scientists refer to as an asymmetric network -- the links are directional, they drawn with arrows.  Links between people on Twitter show direction of intent. The arrows are drawn from source to target.

 

Looking at a social graph from Twitter we can tell a lot by following the arrows... 

 

who is aware of whom/what?

whom/what is getting attention?

who is involved in conversations on specific topics?

who is central, and who is peripheral to the discussions?

 

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"Workshop: Mapping social media spaces" -Marc Smith- SSW12

"Workshop: Mapping social media spaces" -Marc Smith- SSW12 | Social Network Analysis #sna | Scoop.it
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The 50 Most Influential Networking Tycoons | Wealth-X

The 50 Most Influential Networking Tycoons | Wealth-X | Social Network Analysis #sna | Scoop.it
  Bill Gates tops the list with his social graph worth an estimated US$261 billion, which is approximately four times his net worth. Coming in second is
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Mapping the Relationships between the Artists who Invented Abstraction - information aesthetics

Mapping the Relationships between the Artists who Invented Abstraction - information aesthetics | Social Network Analysis #sna | Scoop.it
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The rich club phenomenon in the classroom : Scientific Reports : Nature Publishing Group

The rich club phenomenon in the classroom : Scientific Reports : Nature Publishing Group | Social Network Analysis #sna | Scoop.it
We analyse the evolution of the online interactions held by college students and report on novel relationships between social structure and performance.
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Social Network Analysis and Knowledge Management

Social Network Analysis and Knowledge Management | Social Network Analysis #sna | Scoop.it
Social Network Analysis and Knowledge Management
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Leaders Online's curator insight, May 15, 2013 2:20 AM

Presentatie waarin social netwerk analyse duidelijk wordt uitgelegd. 

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Twitter data on Spanish 2011 protest

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15m.zip contains this “readme.txt” plus two files with information about Twitter messages that were collected in the period April-May 2011. These messages are related to the political events occurred at that time in Spain, and only messages in Spanish are considered.

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Social Network Analysis, a Ph.D. level course

Social Network Analysis, a Ph.D. level course | Social Network Analysis #sna | Scoop.it

Social Network Analysis, a Ph.D. level course taught each Spring by Steve Borgatti under the auspices of the University of Kentucky LINKS Center

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Creation Nets and the Future of Social Networks | gnovis

Georgetown University's peer-reviewed Journal of Communication, Culture & Technology (CCT)
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The researchers used such linguistic measures as how an individual uses the first person singular pronoun (e.g. “I”, “I’ve”, “me”, “mine”), which has been shown to increase as a speaker interacts with someone of higher status. The researchers found that the technical skill-based roles, such as those of programmers and analysts, elicited higher levels of respect than those of more managerial roles

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#NodeXL Exploring Twitter's Social Graph from the comfort of your Windows PC/Excel

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How to Use the “Network Density” Formula to Measure the Health of a Community

How to Use the “Network Density” Formula to Measure the Health of a Community | Social Network Analysis #sna | Scoop.it

A lot of community managers just go with their gut on this one, or use proxy metrics like signups, posts per day, klout scores, retweets or some other metric that is fairly hollow, but there are better ways.

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luiy's curator insight, March 14, 2013 9:27 AM

How can you determine the health of a community?

A lot of community managers just go with their gut on this one, or use proxy metrics like signups, posts per day, klout scores, retweets or some other metric that is fairly hollow, but there are better ways.

This is very much a work in progress, so I’d love to collaborate. If anyone has any thoughts, please jump in the comments sections and let’s discuss. That being said, most of this isn’t new, it’s just stolen, adapted and generally simplified from concepts like Network Theory, Affinity Groups, Clustering Coefficients, Small World Networks, and other things I will never fully understand or convince people to invest tech into.

Let’s dig in…

What is Network Density?

First off, Network Density (ND for short) isn’t one number, it’s more like blood pressure where they say “80 over 120″. I have no idea what the 80 or the 120 mean, but it works as an analogy. So, with that in mind, ND breaks down roughly as:

Average Distance Between Users : Number of Paths : Frequency of Interactions

or simply put…

AD : NP : F

Lets break each part down…

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Using social data to predict Startup Weekend attendees

Using social data to predict Startup Weekend attendees | Social Network Analysis #sna | Scoop.it
This post is the first one from the category “show me your network and I’ll tell you a story”. My goal here is to show how one can grow a business by leveraging publicly available data, mostly from...
ukituki's insight:

Once we have our interest graph we can run some computations and understand:

who are our potential clients/attendeeswho can help us spread the word about the event and reach non-random audiencewho are the most influential users in our target market in general and who are the stars from the local scene
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Who controls the world? More resources for understanding

Who controls the world? More resources for understanding | Social Network Analysis #sna | Scoop.it
James Glattfelder published the study "The Network of Global Corporate Control” as Occupy Wall Street grew. Some resources for understanding.
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#ChaosIsGood: Using Social Networking Analysis To Measure Influence

#ChaosIsGood: Using Social Networking Analysis To Measure Influence | Social Network Analysis #sna | Scoop.it
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HR Trend Institute's curator insight, February 9, 2013 12:18 AM

Chaos is good! 

Andries Du Plessis's comment, February 27, 2013 7:58 AM
Once we understand where the influencers are in a network we can start to undertand how to manage them, right? Im surely curious to understand the new cohort of students' networks
luiy's curator insight, March 14, 2013 9:31 AM

Centrality 

Often, regardless of the industry or organization performing social networking analysis, it is important to understand which models govern their specific target network. It is also critical to understand the smaller, local relationships between the actors (nodes). For example, for intelligence analysis purposes, it is critical to identify how information flows through the network and which nodes are the most active in collecting or sharing information. As such, a centrality of a network describes how important/influential a node is to a network.

 

 Highly central networks operate similar to highly centralized governments such as theocracies or monarchies while least centralized networks mimic democratic system of governments. The centralization of a network is approximately an average of the maximum centrality of a single node over the entire network and can be calculated by Freeman’s general formula.  For practical purposes, it is not always required to calculate this number to be able to realize the centrality of a network. For example, comparing today’s terrorist groups to traditional ones it can be observed without going through the calculations that today’s groups are much less centralized and hence harder to target. Low centralized networks, though sometimes not as effective in terms of governance and implementation of an overall strategy, are much more resilient (‘anti-fragile’) to shocks. For example, it’s much easier to contain a virus in a highly centralized network than it is in a low centralized network. The other concepts of centrality: ‘Closeness’ and ‘Betweenness’ attempt to measure the minimum number of nodes information or a meme would have to travel to get from one node to another. A very close network with many well-connected nodes (‘Betweenness’) would be much better and faster in communicating certain information, virus, knowledge, tradition, and meme across its entire network. A network with a very low ‘Closeness’ would hence be less effective and efficient in doing the same. 

Influence 

One of the most important outcomes of SNA is determining influencers across a network, as well as their level of influence. There are various ways to locate influencers such as number of followers, friends or connections as well as level of activity on social media. However more models are needed to better locate influencers. 
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Using social graphs to understand enterprise social network usage (part 1)

Using social graphs to understand enterprise social network usage (part 1) | Social Network Analysis #sna | Scoop.it

One of the best ways of understanding precisely how your enterprise social network is being used is to visualize the activity using a social graph.

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Calling patterns in human communication dynamics

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Modern technologies not only provide a variety of communication modes, e.g., texting, cellphone conversation, and online instant messaging, but they also provide detailed electronic traces of these communications between individuals. These electronic traces indicate that the interactions occur in temporal bursts.


Here, we study the inter-call durations of the 100,000 most-active cellphone users of a Chinese mobile phone operator. We confirm that the inter-call durations follow a power-law distribution with an exponential cutoff at the population level but find differences when focusing on individual users. We apply statistical tests at the individual level and find that the inter-call durations follow a power-law distribution for only 3460 individuals (3.46%). The inter-call durations for the majority (73.34%) follow a Weibull distribution.

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Twitter mining

Microblog(Twitter) mining yutao
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