Social Network Analysis #sna

Today, P J Miranda at the Federal Technological University of Paraná in Brazil and a couple of pals study the social network between characters in Homer’s ancient Greek poem, the Odyssey.

Their conclusion is that this social network bears remarkable similarities to Facebook, Twitter and the like and that this may offer an important clue about the origin of this ancient story.

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed behave like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection may lead to significant advances in computational social science, social media analytics, and marketing applications.

The usefulness of understanding relationships within networks is becoming more apparent, so it is fortunate that our ability to explore and analyze networks by visualizing them is improving. Common examples of networks that analysts examine include connections between terrorists or connections between linked sites on the World Wide Web. While these networks in particular get a great deal of attention today, other more run-of-the-mill networks can be explored more insightfully as well, such as the connections between products that are often purchased together, which we’ve pursued as market-basket analysis for ages. The most common and typically most useful form of network visualization consists of nodes (things, such as people or products) and links (connections between things), displayed as a diagram in various arrangements. When networks are large, consisting of thousands or millions of nodes, node-link diagrams can become so overwhelmingly cluttered, they’re sometimes called “giant hairballs.” Consequently, those who study information visualization have been trying to develop ways to simplify and clarify these diagrams. A new approach described in a paper titled

Ever since The Guardian reported that the National Security Agency (NSA) has been collecting the phone record metadata of millions of Americans, the cable talk circuit has been ablaze with pundits demanding answers to what should be obvious questions.

Who knew about the program to collect data? (Apparently, all three branches of government). Who else has been supplying data?  (Just about everybody,according to the Washington Post). What is metadata?  (It’s data about data).

The question that nobody seems to be asking is probably the most important one:  What is the NSA doing with the data and why do they need so much of it?  The answer is a relatively new field called social network analysis and, while it may make people uneasy, the benefits far outweigh the risks, so it is probably something we will just have to accept.

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