We focus on studying prediction problems from event sequences. The latter are ubiquitous in several scenario involving human activities, including especially information diffusion in social media.

The scope of the MP is to investigate methods for learning deep probabilistic models based on latent representations that can explain and predict event evolution within social media. Latent variables are particularly promising in situations where the level of uncertainty is high, due to their capabilities in modeling the hidden causal relationships that characterize data and ultimately guarantee robustness and trustability in decisions. In addition, probabilistic models can efficiently support simulation, data generation and different forms of collaborative human-machine reasoning.

There are several reasons why this problem is challenging. We plan to study these challenges and provide an overview of the current advances, as well as a repository of available techniques and datasets that can be exploited for research and study.


A journal paper reviewing the current issues and challenges

A repository of the existing methods, with their implementations and available datasets

Project Partners:

  • Consiglio Nazionale delle Ricerche (CNR), Giuseppe Manco
  • INESC TEC, Joao Gama
  • Università di Pisa (UNIPI), Dino Pedreschi


Primary Contact: Giuseppe Manco, CNR

Results Description

Our microproject aims at investigating methods for modeling event interactions through temporal processes. We revisited the notion of event modeling and provided the mathematical foundations that characterize the literature on the topic. We defined an ontology to categorize the existing approaches in terms of three families: simple, marked, and spatio-temporal point processes. For each family, we systematically reviewed the existing approaches providing a deep discussion. Specifically, we investigated recent machine and deep learning-based methods for modeling temporal processes. We focused on studying prediction problems from event sequences to understand their structural and temporal dynamics. In fact, understanding these dynamics can provide insights into the complex patterns that govern the process and can be used to forecast future events. Among existing approaches, we investigated probabilistic models based on latent representations that represent an appropriate choice to model event sequences. Event sequences are pervasive in several application contexts, such as business processes, smart industry as well as scenarios involving human activities, including especially information diffusion in social media. Indeed, our study has been focused on works whose aim is the prediction of events within social media. Social media focus on the interactions among individuals within context-sharing platforms such as Twitter, Instagram, etc. Interactions can be modeled as event sequences since events can be user actions over time. In addition, we also provided an overview of other application scenarios such as healthcare, finance, disaster management, public security, and daily life. The analyzed literature provides several datasets that we categorized according to the application scenarios they can be used for. For each dataset, we reported its description, the papers containing experiments over it, and, when available, a source web link.


ACM Computing Surveys – Under review

Links to Tangible results

https://github.com/Angielica/temporal_processes: A list of Point Processes resources.
https://github.com/Angielica/datasets_point_processes: A list of relevant datasets.