I completely agree with Lauren on this reading. It started off making sense and being quite clear, then got all sciencey and physicsy and stuff (there’s a reason I’m doing Media, not BioMed…………..)

HOWEVER, the points of the reading that didn’t completely fly over my head were quite interesting. It really helped me wrap my head around the concept of ‘nodes’ and further my understanding of hypertexts.

I did Business Management in year 11 and 12 and I remember learning about the 80/20 rule in terms of business. The rule applied in a number of different factions across management such as 80% of profits come from 20% of customers, 80% of sales are from 20% of your products and 80% of sales come from 20% of your sales staff. Essentially, in any situation, 80% of effects come from 20% of causes. Some examples used in the reading in regards to the network are that 80% of links on the Web point to only 15% of Webpages and 80% of citations go to only 38% of scientists.

Only a small number of ‘nodes’ in the network (I’m assuming this is referring to webpages) have an extraordinarily large number of links and a massive number only have a few links.

A highway road map and an airline map both contain ‘hubs’ and ‘nodes’ but are quite different. They can help demonstrate the structural differences between a random network and one using a power-law degree distribution. In a highway road map, there are many cities or ‘nodes’ and the highways connecting them are the ‘links’. This is a fairly uniform network; each city has a link to the highway system and there are no cities that are served by hundreds of highways. Contrasting this is a airplane map whereby there are some major hubs that have hundreds of links, and many many small airport ‘hubs’ that have barely any links.

This has really helped me understand how the concept of a hypertext works in terms of the network; with its many different links, hubs and nodes that work under either a ‘random’ unstructured network or one using a power-law degree distribution (a more structured one) to connect everything together.

There were a few points that particularly stuck out to me in this reading, so in risk of making another thesis-post, I’ll just discuss them.

Amazon.com was brought up again. Blood Amazon and it’s revolutionary mass communication practices. “Recommendations” according to what you’ve previously bought – sounds so simple but is relatively new; and completely changed the way that consumers shop online. Suddenly, it wasn’t about mainstream tastes, best sellers lists or general marketing practices, it was about the interest and profile of the individual consumer based on algorithm-fuelled technology.

By having an unlimited selection of music, books, movies and TV shows, we’re no longer classed as a mass market obsessed with blockbusters and cringe-worthy summer pop hits; we’re no longer seen as a giant wave of humans who want and need the same things. This was a result of poor supply-and-demand matching – a market response to inefficient distributions of media in a hit-driven culture. We’re now being broken down into groups of consumers with niche tastes who have unlimited access to all the media we could possibly be interested in.

” For consumers, the improved signal-to-noise ratio that comes from following a good recommendation encourages exploration and can reawaken a passion for music and film, potentially creating a far larger entertainment market overall.”

Entertainment is now widely available to us over the internet, Netflix, iTunes, Amazon etc. This has changed the market.

That’s essentially my reading of the article.