Graphs are ubiquitous in encoding relational information of real-world objects in many
domains. Graph generation, whose purpose is to generate new graphs from a distribution …

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 …

Graph generation has shown great potential in applications like network design and mobility
synthesis and is one of the fastest-growing domains in machine learning for graphs. Despite …

Ordinal regression (OR) aims to construct the classifier from data with ordered class labels.
At present, most of the OR methods consider the OR problem as a single learning task and …

The forecasting of significant societal events such as civil unrest and economic crisis is an
interesting and challenging problem which requires both timeliness, precision, and …

Human events such as hospital visits, protests, and epidemic outbreaks directly affect
individuals, communities, and societies. These events are often influenced by factors such …

Due to the potentially significant benefits for society, forecasting spatio-temporal societal
events is currently attracting considerable attention from researchers. Beyond merely …

The surveillance and preventions of infectious disease epidemics such as influenza and
Ebola are important and challenging issues. It is therefore crucial to characterize the disease …

Multi-task learning (MTL) is a framework that enforces multiple learning tasks to share their
knowledge to improve their generalization abilities. While shallow multi-task learning can …

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 …