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 …
Modeling information cascades in a social network through the lenses of the ideological leaning of its users can help understanding phenomena such as misinformation propagation and confirmation bias, and devising techniques for mitigating their toxic …
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 …
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 . …
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 …
User recommender systems are a key component in any on-line social networking platform: they help the users growing their network faster, thus driving engagement and loyalty.
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. …
We study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that experimentally result to be more accurate in describing real-world cascades than the standard propagation models …