The links are much appreciated. Thanks! The social network article is especially detailed , glad you pointed it out.
Funny, I was thinking of the exact same example of modeling diffusion of innovations when you edited your post to include that. Using a social network, you could simulate the diffusion of information about a product from the starting point of an advertisement. So theoretically, you could highly optimize your ad campaign to target just the right people that will spread the word of your product the farthest.
Using a social network, you could simulate the diffusion of information about a product from the starting point of an advertisement. So theoretically, you could highly optimize your ad campaign to target just the right people that will spread the word of your product the farthest.
Yep. Gladwell talks about the marketing aspect of some of these ideas in this book The Tipping Point. Actually, despite being a "pop science" book, there's a lot of good stuff in The Tipping Point, in terms of providing good starting points to start exploring. Reading his book was one of the things that got me interested in this field. From there, I started reading the stuff by Duncan Watts and Albert-László Barabási and then some of the more technical stuff. It turns out that network science underlies and unifies all sorts of stuff. It's really proving to be fascinating... well, to me, anyway.
They actually used some online networks for their simulations:
"We investigate (i) the friendship network between 3.4 million members of the LiveJournal.com community [15], (ii) the network of email contacts in the Computer Science Department of the University College London (Zhou, S., private communication), (iii) the contact network of inpatients (CNI) collected from hospitals in Sweden [16], and (iv) the network of actors who have co-starred in movies labeled by imdb.com as adult [17]"
They found some rules about how the structure of the network influences the spreading ability of each node. As you may suspect, they find highly connected individuals aren't necessarily the best spreaders.
Comments
The links are much appreciated. Thanks! The social network article is especially detailed , glad you pointed it out.
Funny, I was thinking of the exact same example of modeling diffusion of innovations when you edited your post to include that. Using a social network, you could simulate the diffusion of information about a product from the starting point of an advertisement. So theoretically, you could highly optimize your ad campaign to target just the right people that will spread the word of your product the farthest.
Using a social network, you could simulate the diffusion of information about a product from the starting point of an advertisement. So theoretically, you could highly optimize your ad campaign to target just the right people that will spread the word of your product the farthest.
Yep. Gladwell talks about the marketing aspect of some of these ideas in this book The Tipping Point. Actually, despite being a "pop science" book, there's a lot of good stuff in The Tipping Point, in terms of providing good starting points to start exploring. Reading his book was one of the things that got me interested in this field. From there, I started reading the stuff by Duncan Watts and Albert-László Barabási and then some of the more technical stuff. It turns out that network science underlies and unifies all sorts of stuff. It's really proving to be fascinating... well, to me, anyway.
Here's a link to some interesting theoretical work by Hernan Makse titled "Identifying Influential Spreaders in Complex Networks."
http://arxiv.org/PS_cache/arxiv/pdf/1001/1001.5285v2.pdf
They actually used some online networks for their simulations:
"We investigate (i) the friendship network between 3.4 million members of the LiveJournal.com community [15], (ii) the network of email contacts in the Computer Science Department of the University College London (Zhou, S., private communication), (iii) the contact network of inpatients (CNI) collected from hospitals in Sweden [16], and (iv) the network of actors who have co-starred in movies labeled by imdb.com as adult [17]"
They found some rules about how the structure of the network influences the spreading ability of each node. As you may suspect, they find highly connected individuals aren't necessarily the best spreaders.