Social Network Analysis #sna

About the Social Network Analysis Interactive Dataset Library

This site contains an accessible library of many of the 'open' social network analysis datasets . This library of datasets is open to all, and anyone can add datasets - as a consequence the quantity and quality of the library is growing pretty quickly.

This interactive library provides researchers with an accessible overview of the different type of open social network datasets available. It was initially developed as part of a research project to outline the different types of social network datasets at the Dynamics lab in University College Dublin. As of our launch data (April 2013) some datasets have had their features extensively catalogued, while others have just the bare minimum details.

In total there are currently 173 Networks in the library. New datasets can be added by anyone (beginning here) and existing datasets can be edited on their overview page (for an example, see here).

There is a tabular view (summary), an interactive visualisation, or you can simply download all the library data. Note that this is an ongoing effort, and there are many publicly available network datasets not yet captured here, and some of the datasets captured within have not had their details populated yet.

Our objective is to create a open resource that contains information about available social network datasets, including the key features (e.g. are the networks multimodal, bipartite, multiplex, dynamic etc.) and size (number of nodes, number of edges). This resource will prove useful to both those beginning to think about social networks and those who may be seeking a dataset of very specific structure / size (e.g. in order to test an algorithm).

If you do social network analysis #SNA here's a source of free data sets.

We develop a social network model of occupational segregation between different social groups, generated by the existence of positive inbreeding bias among individuals from the same group. If network referrals are important in getting a job, then expected inbreeding bias in the
contact network structure induces different career choices for individuals
from different social groups. This further translates into stable occupational segregation equilibria in the labour market. We derive the conditions for persistent wage and unemployment inequality in the segregation
equilibria. Our framework is proposed as complementary to existing theories used to explain labour market inequalities between groups divided by race, ethnicity or gender.

Un muy interesante artículo sobre el Análisis de Redes

Social Network analysis allows us to identify players in a social network and how they are related to each other. For example: I want to identify people who are involved in a certain topic - either to interview or to understand what different groups are engaging in debate.

What you’ll Need:

Throughout this exercise we will use Gephi for graph analysis and visualization. Let’s start by getting a small graph into gephi.

Take a look at the sample spreadsheet - this is data from a fictional case you are investigating.

In your country the minister of health (Mark Illinger) recently bought 500,000 respiration masks from a company (Clearsky-Health) during a flu-scare that turned out non substantial. The masks were never used and rot away in the basement of the ministry. During your investigation you found that during the period of this deal Clearsky-Health was consulted by Flowingwater Consulting and paid them a large sum for their services. A consulting company owned by Adele Meral-Poisson. Adele Meral-Poisson is a well known lobbyist and the wife of Mark Illinger.

While we don’t need to apply network analysis to understand this fictional case - it helps understanding the sample spreadsheet. Gephi is able to import spreadsheets like this through it’s “import csv” section. Let’s do this.

JamesMTitus was manufactured by cyber-security specialists in New Zealand participating in a two-week social-engineering experiment organized by the Web Ecology Project. Based in Boston, the group had conducted demographic analyses of Chatroulette and studies of Twitter networks during the recent Middle East protests. It was now interested in a question of particular concern to social-media experts and marketers: Is it possible not only to infiltrate social networks, but also to influence them on a large scale?

The group invited three teams to program “social bots”—fake identities—that could mimic human conversation on Twitter, and then picked 500 real users on the social network, the core of whom shared a fondness for cats. The Kiwis armed JamesMTitus with a database of generic responses (“Oh, that’s very interesting, tell me more about that”) and designed it to systematically test parts of the network for what tweets generated the most responses, and then to talk to the most responsive people.

After the first week, the teams were allowed to tweak their bot’s code and to launch secondary identities designed to sabotage their competitors’ bots. One team unleashed @botcops, which alerted users, “You might want to be suspicious about JamesMTitus.” In one exchange, a British user confronted the alleged bot: “What do you say @JamesMTitus?” The robot replied obliquely, “Yeah, so true!” The Brit pressed: “Yeah so true! You mean I should be suspicious of you? Or that @botcops should be challenged?” JamesMTitus evaded detection with a vague tweet back—“Right on bro”—and acquired 109 followers over two weeks. Network graphs subsequently showed that the three teams’ bots had insinuated themselves into the center of the target network.

We identify influential early adopters that achieve a target behavior distribution for a resource constrained social network with multiple costly behaviors. This problem is important for applications ranging from collective behavior change to corporate viral marketing campaigns. In this paper, we propose a model of diffusion of multiple behaviors when individual participants have resource constraints. Individuals adopt the set of behaviors that maximize their utility subject to available resources. We show that the problem of influence maximization for multiple behaviors is NP-complete. Thus we propose heuristics, which are based on node degree and expected immediate adoption, to select early adopters. We evaluate the effectiveness under three metrics: unique number of participants, total number of active behaviors and network resource utilization. We also propose heuristics to distribute the behaviors amongst the early adopters to achieve a target distribution in the population. We test our approach on synthetic and real-world topologies with excellent results. Our heuristics produce 15-51\% increase in resource utilization over the na\"ive approach.