CS 224W Project Proposal

Introduction

Survey

Reaction

Facebook Location Paper

Notes:

Jure’s Contagion paper:

Notes and Thoughts:

* modelling contagions as being conditionally independent, loosing some info that way
        - Instead why not have two sets of contagions; one is like a max heap where the ones that have had most influence over you exist and the other set models the recency effect where contagions you have interacted with most recently have a bigger weight. 
* a highly infectious URL can help a less infectious but highly correlated url go viral while it can suppress a slightly less infectious but unrelated URL. 

Understanding the model they have used

*Consider probability of a user adopting a piece of content based on what they have seen before
* Estimate the prob of a user being infected by a contagion given the sequence of contagions she has been exposed to so far.
* Can we make an improvement in the model by modeling the dependence of the contagions. We are saying X is conditioned on Y1 to Yk but a time step ago Y1 was X. Pre computing or some sort of sampling can help us find the joint probability distribution rather than assuming independence. This seems to be a standard hand waving that is going on
*Contagion interaction function models the effect contagion uj has on our current contagion ui when ui was exposed to uj k exposures ago. 
*A way to model this will be in a k different WXW matrices where rows represent different possible ui's and columns uj. 
* So the best way to do this is to identify classes or clusters to which each contagion belongs and model the interactions between these classes. 
*A way to set up this class is via a Membership matrix where which is of size W X T where W is number of contagions and T is the number of clusters(classes) , here M(i,j) is the probability that contagion ui is a member of cluster cj. 

This new matrix gives rise to a new interaction function. Instead of modelling the interaction between two contagions we are now modeling the interaction between two clusters scaled by the probablities.

* Therefore now to find the interaction between any two clusters: For any contagions ui and uj we find the probability that ui belongs to any class/cluster t and for any such case we find the probability that uj belongs to any class/cluster s and inside a double summation we multiply the above two probabilities with the interaction function for these two clusters s and t.  

Didn’t understand how their clustering matrix is a low rank

Questions:

Uncovering structure of nested/Overlapping Communities

Notes:

Conclusion:

MemeTracking and the Dynamics of the News Cycle

Notes:

Romero’s Paper

Patterns of Temporal Variation in Online Media

Project Proposal

References

Experiments

Papers Read:

CS 229 Project Readings

Evaluation and Analysis of Personalised searches.

Notes: