In this paper we propose a survival factorization framework that models information cascades by tying together social influence patterns, topical structure and temporal dynamics. This is achieved through the introduction of a latent space which …
We tackle the problem of predict whether a target user (or group of users) will be active within an event stream before a time horizon. Our solution, called PATH, leverages recurrent neural networks to learn an embedding of the past events. The …
In this paper we propose a survival factorization framework that models information cascades by tying together social influence pat- terns, topical structure and temporal dynamics. This is achieved through the introduction of a latent space which …
We study the problem of detecting social communities when the social graph is not available but instead we have access to a log of user activity, that is, a dataset of tuples (u, i, t) recording the fact that user u “adopted” item i at time t. We …
How can we detect communities when the social graphs is not available? We tackle this problem by modeling social contagion from a log of user activity, that is a dataset of tuples (u, i, t) recording the fact that user u "adopted" item i at time t. …