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Social Network Analysis

Social Network Analysis
Brief Description: "Social network analysis is the mapping and measuring of relationships and flows between people, groups, organisations, computers or other information/knowledge processing entities." (Valdis Krebs, 2002). Social Network Analysis (SNA) is a method for visualizing our people and connection power, leading us to identify how we can best interact to share knowledge. A related practice is Network Mapping. History: When to use: How to use: From the UK's NHS KM Library: "In the context of knowledge management, social network analysis (SNA) enables relationships between people to be mapped in order to identify knowledge flows: who do people seek information and knowledge from? Informal Face to Face Network Mapping To highlight the value of our social networks, at a face to face gathering create an informal map using Post-it notes on a large piece of paper. Tools and Software for SNA Highly recommend. Training on SNA Tips and Lessons Learnt Tags Related:  Social Network Systems

The Emerging Science of Superspreaders (And How to Tell If You're One Of Them) Who are the most influential spreaders of information on a network? That’s a question that marketers, bloggers, news services and even governments would like answered. Not least because the answer could provide ways to promote products quickly, to boost the popularity of political parties above their rivals and to seed the rapid spread of news and opinions. So it’s not surprising that network theorists have spent some time thinking about how best to identify these people and to check how the information they receive might spread around a network. Indeed, they’ve found a number of measures that spot so-called superspreaders, people who spread information, ideas or even disease more efficiently than anybody else. But there’s a problem. But there is growing evidence that information does not spread through real networks in the same way as it does through these idealised ones. So the question of how to find the superspreaders remains open.

ation: Provocative Ideas (An excerpt from Imaginization: The Art of Creative Management ) "The Gulf" is very common in what I call "Model 3" organizations. These are organizations that, like Stereotype (a case study discussed in Imaginization: The Art of Creative Management), get stuck in their attempts to change because they are caught by the old bureaucratic forces they are attempting to shake off. I have developed the "Model 3" concept as one of six models of organization designed to characterize organizational forms suited to different degrees of environmental change, and illustrate the difficulties that can be encountered in trying to shift from one form of organization to another. The six templates provide rough models against which people can identify the characteristics of their own organizations or organizational subunits and understand the generic nature of their particular difficulties in managing organizational change. Model 2 works reasonably well for dealing with moderate amounts of change.

Measuring Large-Scale Social Networks with High Resolution This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. Figures Editor: Yamir Moreno, University of Zaragoza, Spain Copyright: © 2014 Stopczynski et al.

Community of action A community of action (CoA), unlike a community of practice (CoP), exists in a situation that is structurally more open, where actors have the possibility of bringing about change. These more open situations might, for example, correspond to collective design teams in professional environments. CoAs possess some of the characteristics of communities, such as the development of a common language and mutual learning in the course of action. However, they also possess some of the characteristics typical of more associative social relationships, such as the “voluntary” nature of association and the importance of “common goals” in directing collective activity. Some argue that this makes CoAs more “rational” groups than CoPs. Related to[edit] External links[edit] Facebook feelings are contagious: Study examines how emotions spread online You can’t catch a cold from a friend online. But can you catch a mood? It would seem so, according to new research from the University of California, San Diego. Published in PLOS ONE, the study analyzes over a billion anonymized status updates among more than 100 million users of Facebook in the United States. Positive posts beget positive posts, the study finds, and negative posts beget negative ones, with the positive posts being more influential, or more contagious. “Our study suggests that people are not just choosing other people like themselves to associate with but actually causing their friends’ emotional expressions to change,” said lead author James Fowler, professor of political science in the Division of Social Sciences and of medical genetics in the School of Medicine at UC San Diego. There is abundant scientific literature on how emotion can spread among people – through direct contact, in person – not only among friends but also among strangers or near-strangers.

Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters Polarized Crowds: Political conversations on Twitter Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. If a topic is political, it is common to see two separate, polarized crowds take shape. While these polarized crowds are common in political conversations on Twitter, it is important to remember that the people who take the time to post and talk about political issues on Twitter are a special group. Still, the structure of these Twitter conversations says something meaningful about political discourse these days and the tendency of politically active citizens to sort themselves into distinct partisan camps. Our approach combines analysis of the size and structure of the network and its sub-groups with analysis of the words, hashtags and URLs people use. Conversational archetypes on Twitter Figure 2 Figure 3

Applying algorithm to social networks can reveal hidden connections criminals use to commit fraud Fraudsters beware: the more your social networks connect you and your accomplices to the crime, the easier it will be to shake you from the tree. The Steiner tree, that is. In an article recently published in the journal Computer Fraud and Security, University of Alberta researcher Ray Patterson and colleagues from the University of Connecticut and University of California – Merced outlined the connection linking fraud cases and the algorithm designed by Swiss mathematician Jakob Steiner. Fraud is a problem that costs Canadians billions of dollars annually and countless hours of police investigations. Patterson says that building the algorithm into fraud investigation software may provide important strategic advantages. The criminal path of least resistance To quote a television gumshoe, everything's connected. "You're really trying to find the minimum set of connectors that connect these people to the various [network] resources," he said. Fraud and the Steiner tree, by the numbers

A search engine for social networks based on the behavior of ants Research at Carlos III University in Madrid is developing an algorithm, based on ants' behavior when they are searching for food, which accelerates the search for relationships among elements that are present in social networks. One of the main technical questions in the field of social networks, whose use is becoming more and more generalized, consists in locating the chain of reference that leads from one person to another, from one node to another. The greatest challenges that are presented in this area is the enormous size of these networks and the fact that the response must be rapid, given that the final user expects results in the shortest time possible. In order to find a solution to this problem, these researchers from UC3M have developed an algorithm SoSACO, which accelerates the search for routes between two nodes that belong to a graph that represents a social network. Multiple applications

Computational social science: Making the links Jon Kleinberg's early work was not for the mathematically faint of heart. His first publication1, in 1992, was a computer-science paper with contents as dense as its title: 'On dynamic Voronoi diagrams and the minimum Hausdorff distance for point sets under Euclidean motion in the plane'. That was before the World-Wide Web exploded across the planet, driven by millions of individual users making independent decisions about who and what to link to. “I realized that computer science is not just about technology,” he explains. Kleinberg is not alone. “It's been really transformative,” says Michael Macy, a social scientist at Cornell and one of 15 co-authors of a 2009 manifesto4 seeking to raise the profile of the new discipline. Nonetheless, says Macy, “I liken the opportunities to the changes in physics brought about by the particle accelerator, and in neuroscience by functional magnetic resonance imaging”. Social calls Infectious ideas Message received

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