Events are occurrences in specific locations, time, and semantics that nontrivially impact
either our society or the nature, such as earthquakes, civil unrest, system failures …

Modeling concurrent events of multiple types and their involved actors from open-source
social sensors is an important task for many domains such as health care, disaster relief …

Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-
care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease …

Event forecasting with an aim at modeling contextual information is an important task for
applications such as automated analysis generation and resource allocation. Captured …

Spatio-temporal societal event forecasting, which has traditionally been prohibitively
challenging, is now becoming possible and experiencing rapid growth thanks to the big data …

Deep learning models have been studied to forecast human events using vast volumes of
data, yet they still cannot be trusted in certain applications such as healthcare and disaster …

Events can be defined as phenomena that occur at a specific time and place. Social
networks and news portals publish thousands of events daily, and this knowledge is …

Electric grid continually monitors spatiotemporal data from sparse service areas. As power
systems grow and get more complex, and with the deployment of more sensors and data …

Event detection is a critical task for timely decision-making in graph analytics applications.
Despite the recent progress towards deep learning on graphs, event detection on dynamic …

Predicting multiple concurrent events has a remarkable effect on understanding social
dynamics and acting in advance to reduce damage.(1) From the perspective of spatial …