I have a new revision available on arxiv of the paper What stops social epidemics? I’ll be presenting the paper at ICWSM 2011 in Barcelona.

How likely is it for a virus or piece of information to spread through a network? If, on average, each person spreads the virus to more than one person than the virus will propagate through the entire network, an epidemic. If, on the other hand, each person spreads it to less than one person, the outbreak will die out very quickly, most likely after a handful of people have seen it.

For social epidemics on the Digg network, neither of these things happened. Stories would spread to a few hundred friends in the network, then stop without reaching even 0.1% of the whole network. There are many reasons for the slow-down of epidemics, but we identified two crucial, complementary ingredients.

• Most people who are exposed to a story are exposed by multiple friends
• More friends exposing you to a story does not make you more likely to spread the story yourself

Why does this slow down the epidemic? The first (primary) story spreader exposes some number of new people. Some fraction of those people are interested and spread the story themselves. If the people following these new (secondary) spreaders were a new, totally random group of people that looked just like the one the first spreader had, the story would merrily keep spreading. This kind of assumption is, roughly, a “mean field approximation”. But that’s not what happens. Instead, many of the friends of the secondary spreaders were already exposed by the primary spreader. Those people didn’t spread the story after one exposure, and they will continue to ignore it now. Fewer new people are reached in each round of spreading until the story dies out with a whimper.

A lovely day at the Huntington.

I had posters in two sessions:

Machine learning for social computing

and

Networks across disciplines

I got some great feedback and actually managed to understand a few talks. One interesting thing that I saw a few presentations about involved decomposing a matrix as a sum of a low rank and a sparse matrix. This can be cast as everybody’s favorite thing, and SDP! And it seems a very intuitive approach towards noisy data. I’d also like to read up about some nice posters I saw on efficient MMSBs and structure learning structure for sparse graphical models.

I almost missed my wordpress domain mapping expiring, I guess that’s what happens when you never blog.

What has happened since March? Oh, we bought a house and moved. More friends got their PhDs and moved on. My sister got married. Lots of fun summer stuff in LA. I got rid of my car, got a scooter, and am a week or two away from getting my motorcycle license.

I’m still enjoying research at ISI, but I should really write about that on my even more neglected ISI web page.

I still haven’t done any updating. But I saw a great talk at the ISI retreat about maintaining your research web page. Bibbase automatically converts bibtex files into awesome dynamical web code.

My digital self needs a real facelift.

I’m sort of annoyed that any institution specific research web page vanishes when you leave. Therefore I’m considering either keeping all research related notes on this website or on my nascent academia.edu web page.

In the past year, I finished my PhD in physics at Caltech, did a brief stint at Google, and am now doing research as a postdoc at the Information Science Institute. Somewhere in there I got married, and visited my wife’s family in India.

At the De Young museum, you change the art just by observing it!

In menlo park.

Not bad isi, but you should try not being so far away from Pasadena, the center of the universe.

Some flowers in our garden.