Social Network Analysis #sna
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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 3:18 AM

Chaos is good! 

Andries Du Plessis's comment, February 27, 2013 10:58 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 12:31 PM

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. 
Social Network Analysis #sna
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Psychological Networks Amsterdam Summer School 2017

Psychological Networks Amsterdam Summer School 2017 | Social Network Analysis #sna | Scoop.it
From August 28 to September 1 2017 we held a summer school on network analysis of psychological datasets in Amsterdam (http://psychosystems.org/summerschool2017). Here you will find all slides and exercises
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From August 28 to September 1 2017 we held a summer school on network analysis of psychological datasets in Amsterdam (http://psychosystems.org/summerschool2017). Here you will find all slides and exercises
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The Real Difference Between Google And Apple

The Real Difference Between Google And Apple | Social Network Analysis #sna | Scoop.it
Google and Apple are both hyper-successful companies, but chart their patents, and they have completely different innovation signatures.
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Gephi 3D Network Analysis - Tweets Mentioning Donald Trump  

I'm experimenting with some video for my presentation at the 2016 American Political Science Convention over the weekend of September 1st through 4t
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Connected World: Untangling the Air Traffic Network

Connected World: Untangling the Air Traffic Network | Social Network Analysis #sna | Scoop.it
THE NETWORK




CC-BY-SA Freely reusable with a link to this post

 This
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Eigencentrality based on dissimilarity measures reveals central nodes in complex networks

Eigencentrality based on dissimilarity measures reveals central nodes in complex networks | Social Network Analysis #sna | Scoop.it
One of the most important problems in complex network’s theory is the location of the entities that are essential or have a main role within the network.
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Do online social media cut through the constraints that limit the size of offline social networks? by R. I. M. Dunbar

Do online social media cut through the constraints that limit the size of offline social networks? by R. I. M. Dunbar | Social Network Analysis #sna | Scoop.it
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Network analysis of hazard interconnections – Aaron Clark-Ginsberg

Network analysis of hazard interconnections – Aaron Clark-Ginsberg | Social Network Analysis #sna | Scoop.it
This post is the first in a series focused on using network analysis to analyse disaster risk. Disasters beget disasters. The 2010 Haiti earthquake for...
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How does misinformation spread online?

How does misinformation spread online? | Social Network Analysis #sna | Scoop.it
Recent studies that focus on misinformation online pointed out that the selective exposure to specific contents lead to ‘echo-chambers’ in which users tend to shape and reinforce their beliefs.
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Detecting automation of twitter accounts are you a human, bot, or cyborg

Twitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with each other by publishing text-based p…
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Networks of military alliances, wars, and international trade

The incidence of interstate wars has dropped dramatically over time: The number of wars per pair of countries per year from 1950 to 2000 was roughly a 10th as high as it was from 1820 to 1949. This significant decrease in the frequency of wars correlates with a substantial increase in the number of military alliances per country and the stability of those alliances. We show that one possible explanation of this is an accompanying expansion of international trade. Increased trade decreases countries’ incentives to attack each other and increases their incentives to defend each other, leading to a stable and peaceful network of military and trade alliances that is consistent with observed data.

Networks of military alliances, wars, and international trade Matthew O. Jackson and Stephen Nei

PNAS 112(50):15277–15284

http://dx.doi.org/10.1073/pnas.1520970112 ;


Via Complexity Digest
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Based on the model we also examine some specific relationships, finding that countries with high levels of trade with their allies are less likely to be involved in wars with any other countries (including allies and nonallies), and that an increase in trade between two countries correlates with a lower chance that they will go to war with each other.
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Meritocracy and Topocracy of Networks - Cesar Hidalgo - YouTube

https://youtu.be/CTJ8TAMv3sk
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R tutorial: how to identify communities of items in networks | Psych Networks

R tutorial: how to identify communities of items in networks | Psych Networks | Social Network Analysis #sna | Scoop.it
A problem we see in psychological network papers is that authors sometimes over-interpret the visualization of their data. This pertains especially to the layout and node placement of the graph, for instance: do nodes in the networks cluster in certain communities. Below I will discuss this problem in some detail, …
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A Visual Guide to Graph Traversal Algorithms

A Visual Guide to Graph Traversal Algorithms | Social Network Analysis #sna | Scoop.it
A visual guide to Graph Traversal Algorithms
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Analyzing the Panama Papers with Neo4j: Data Models, Queries & More

Analyzing the Panama Papers with Neo4j: Data Models, Queries & More | Social Network Analysis #sna | Scoop.it
Take a deep dive into the Panama Papers data with Neo4j and discover how to analyze implicit connections between entities, family members and companies.
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WEF16 Davos Twitter Performance Analysis

WEF16 Davos Twitter Performance Analysis | Social Network Analysis #sna | Scoop.it

During the World Economic Forum in Davos 2016 (WEF16) we collected over 480 thousand Tweets with content relating to #wef

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How a small data design company visualized the world's scientific collaborations - Storybench

How a small data design company visualized the world's scientific collaborations - Storybench | Social Network Analysis #sna | Scoop.it
Cuban scientists tend to partner up with Germans, but so do French researchers. The Kenyans work with South Africans. But, unsurprisingly, the global all-stars of scientific collaboration are the United States and China. An interactive map recently published by Nature revealed this web of collaborations to visualize the entire globe’s scientific partnerships. Made up of a constellation of colorful dots superimposed over [...]
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Concurrent Bursty Behavior of Social Sensors in Sporting Events

Concurrent Bursty Behavior of Social Sensors in Sporting Events | Social Network Analysis #sna | Scoop.it
“The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as “social sensors.” Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We confirm that these findings also hold true for tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.”

Takeichi Y, Sasahara K, Suzuki R, Arita T (2015) Concurrent Bursty Behavior of Social Sensors in Sporting Events. PLoS ONE 10(12): e0144646. http://dx.doi.org/10.1371/journal.pone.0144646


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Network theory sheds new light on origins of consciousness

Network theory sheds new light on origins of consciousness | Social Network Analysis #sna | Scoop.it
Vanderbilt University researchers took a significant step toward answering longstanding questions about the origins of consciousness with a recent discovery of global changes in how brain areas communicate with one another during awareness.
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“We know there are numerous brain networks that control distinct cognitive functions such as attention, language and control, with each node of a network densely interconnected with other nodes of the same network, but not with other networks,” Marois said. “Consciousness appears to break down the modularity of these networks, as we observed a broad increase in functional connectivity between these networks with awareness.”

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Modeling financial networks based on interpersonal trust | Open Agent Based Modeling Consortium

Modeling financial networks based on interpersonal trust | Open Agent Based Modeling Consortium | Social Network Analysis #sna | Scoop.it
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Welcome to the Modeling Commons! -- NetLogo Modeling Commons #networks, #complexity

Welcome to the Modeling Commons! -- NetLogo Modeling Commons #networks, #complexity | Social Network Analysis #sna | Scoop.it
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