I'll preface this with the fact that I've not read those papers (but today was thinking through the concept of write hooks in information graphs), but I'd assume from the common formation of the pattern that it's only really set up for one-dimensional publishes.
The problem in large scale information networks, more in the Facebook way than the Twitter way, is that you you potentially trigger a cascading effect in information updates if you go pub-sub. Specifically, applications that do interesting things with social graphs have to go beyond basically doing message passing.
I naturally look at things from a recommendations angle, but if you've got a new edge that enters the graph that may affect other edges that are connected to the end points. Those may in turn affect the edges that are connected to those nodes and so on. You want to avoid something that effectively becomes a breadth first traversal of the graph doing updates since that's well, slow, to put it mildly.
This is why large-scale graph algorithms like PageRank work on constantly regenerating static matrices rather than doing regeneration of the ranks dynamically, but that naturally is problematic when you're working on data sets where the most recent data is the most important and is being generated at very high rates.
In some models, sure, but is that true in Facebook's? Does anything go beyond one degree?
I can't think of anything, really. If X and Y becomes friends, X's friends and Y's friends see it in their feed ("inbox"), but nobody else. Same goes for tagging someone in a photo, etc.
Ad targeting, what's hot in your network, friend recommenders, etc. are all essentially ranking problems. They're moving more in that direction, definitely.
The problem in large scale information networks, more in the Facebook way than the Twitter way, is that you you potentially trigger a cascading effect in information updates if you go pub-sub. Specifically, applications that do interesting things with social graphs have to go beyond basically doing message passing.
I naturally look at things from a recommendations angle, but if you've got a new edge that enters the graph that may affect other edges that are connected to the end points. Those may in turn affect the edges that are connected to those nodes and so on. You want to avoid something that effectively becomes a breadth first traversal of the graph doing updates since that's well, slow, to put it mildly.
This is why large-scale graph algorithms like PageRank work on constantly regenerating static matrices rather than doing regeneration of the ranks dynamically, but that naturally is problematic when you're working on data sets where the most recent data is the most important and is being generated at very high rates.